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End of training

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README.md ADDED
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+ ---
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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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+ - template:sd-lora
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+ widget:
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+
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+ - text: 'a <s0><s1> flower'
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+ output:
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+ url:
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+ "image_0.png"
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+
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+ - text: 'a <s0><s1> flower'
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+ output:
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+ url:
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+ "image_1.png"
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+
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+ - text: 'a <s0><s1> flower'
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+ output:
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+ url:
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+ "image_2.png"
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+
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+ - text: 'a <s0><s1> flower'
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+ output:
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+ url:
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+ "image_3.png"
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+
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+ base_model: stabilityai/stable-diffusion-xl-base-1.0
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+ instance_prompt: a <s0><s1> wave
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+ license: openrail++
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+ ---
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+
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+ # SDXL LoRA DreamBooth - backnotprop/crash-report-waves
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+
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+ <Gallery />
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+
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+ ## Model description
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+
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+ ### These are backnotprop/crash-report-waves LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
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+
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+ ## Download model
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+
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+ ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
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+
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+ - **LoRA**: download **[`crash-report-waves.safetensors` here 💾](/backnotprop/crash-report-waves/blob/main/crash-report-waves.safetensors)**.
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+ - Place it on your `models/Lora` folder.
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+ - On AUTOMATIC1111, load the LoRA by adding `<lora:crash-report-waves:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
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+ - *Embeddings*: download **[`crash-report-waves_emb.safetensors` here 💾](/backnotprop/crash-report-waves/blob/main/crash-report-waves_emb.safetensors)**.
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+ - Place it on it on your `embeddings` folder
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+ - Use it by adding `crash-report-waves_emb` to your prompt. For example, `a crash-report-waves_emb wave`
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+ (you need both the LoRA and the embeddings as they were trained together for this LoRA)
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+
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+
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+ ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
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+
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+ ```py
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+ from diffusers import AutoPipelineForText2Image
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+ import torch
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+ from huggingface_hub import hf_hub_download
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+ from safetensors.torch import load_file
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+
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+ pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
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+ pipeline.load_lora_weights('backnotprop/crash-report-waves', weight_name='pytorch_lora_weights.safetensors')
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+ embedding_path = hf_hub_download(repo_id='backnotprop/crash-report-waves', filename='crash-report-waves_emb.safetensors' repo_type="model")
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+ state_dict = load_file(embedding_path)
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+ pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
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+ pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
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+
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+ image = pipeline('a <s0><s1> flower').images[0]
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+ ```
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+
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+ For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
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+
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+ ## Trigger words
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+
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+ To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
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+
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+ to trigger concept `TOK` → use `<s0><s1>` in your prompt
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+
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+
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+
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+ ## Details
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+ All [Files & versions](/backnotprop/crash-report-waves/tree/main).
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+
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+ The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
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+
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+ LoRA for the text encoder was enabled. False.
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+
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+ Pivotal tuning was enabled: True.
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+
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+ Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
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+
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