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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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+ base_model: stabilityai/stable-diffusion-xl-base-1.0
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+ instance_prompt: tarot_style
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+ tags:
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+ - stable-diffusion-xl
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+ - stable-diffusion-xl-diffusers
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+ - text-to-image
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+ - diffusers
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+ - lora
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+ inference: false
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+ datasets:
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+ - jtlowell/tarot_2
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+ ---
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+
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+ # LoRA DreamBooth - jtlowell/tarot
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+
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+ These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
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+
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+ The weights were trained on the concept prompt:
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+
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+ `tarot_style`
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+
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+ Use this keyword to trigger your custom model in your prompts.
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+
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+ LoRA for the text encoder was enabled: False.
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+ Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
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+
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+ ## Usage
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+
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+ Make sure to upgrade diffusers to >= 0.19.0:
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+ ```
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+ pip install diffusers --upgrade
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+ ```
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+
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+ In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark:
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+ ```
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+ pip install invisible_watermark transformers accelerate safetensors
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+ ```
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+
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+ To just use the base model, you can run:
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+
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+ ```python
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+ import torch
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+ from diffusers import DiffusionPipeline, AutoencoderKL
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+
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+ vae = AutoencoderKL.from_pretrained('madebyollin/sdxl-vae-fp16-fix', torch_dtype=torch.float16)
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+
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+ pipe = DiffusionPipeline.from_pretrained(
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+ "stabilityai/stable-diffusion-xl-base-1.0",
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+ vae=vae, torch_dtype=torch.float16, variant="fp16",
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+ use_safetensors=True
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+ )
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+
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+ # This is where you load your trained weights
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+ pipe.load_lora_weights('jtlowell/tarot')
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+
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+ pipe.to("cuda")
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+
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+ prompt = "A majestic tarot_style jumping from a big stone at night"
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+
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+ image = pipe(prompt=prompt, num_inference_steps=50).images[0]
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+ ```
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