UNSAGroupe
commited on
Upload folder using huggingface_hub
Browse files- .gitattributes +27 -0
- README.md +155 -0
- image-0.png +3 -0
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- logs/dreambooth-lora-sd-xl/1729585266.1896975/events.out.tfevents.1729585266.r-unsagroupe-autotrain-unsalora-e833dgoe-48c16-6nlkx.212.1 +3 -0
- logs/dreambooth-lora-sd-xl/1729585266.1909153/hparams.yml +75 -0
- logs/dreambooth-lora-sd-xl/events.out.tfevents.1729585266.r-unsagroupe-autotrain-unsalora-e833dgoe-48c16-6nlkx.212.0 +3 -0
- pytorch_lora_weights.safetensors +3 -0
- unsalora.safetensors +3 -0
- unsalora_emb.safetensors +3 -0
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README.md
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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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- text: in the style of <s0><s1>
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output:
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url: image-0.png
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- text: in the style of <s0><s1>
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output:
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url: image-1.png
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- text: in the style of <s0><s1>
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output:
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url: image-2.png
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- text: in the style of <s0><s1>
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output:
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url: image-3.png
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- text: in the style of <s0><s1>
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output:
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url: image-4.png
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- text: in the style of <s0><s1>
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output:
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url: image-5.png
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- text: in the style of <s0><s1>
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output:
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url: image-6.png
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- text: in the style of <s0><s1>
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output:
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url: image-7.png
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- text: in the style of <s0><s1>
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output:
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url: image-8.png
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output:
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url: image-9.png
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output:
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url: image-10.png
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output:
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url: image-11.png
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- text: in the style of <s0><s1>
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output:
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url: image-12.png
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- text: in the style of <s0><s1>
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output:
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url: image-13.png
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- text: in the style of <s0><s1>
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output:
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url: image-14.png
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- text: in the style of <s0><s1>
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output:
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url: image-15.png
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- text: in the style of <s0><s1>
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output:
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url: image-16.png
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- text: in the style of <s0><s1>
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output:
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url: image-17.png
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- text: in the style of <s0><s1>
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output:
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- text: in the style of <s0><s1>
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output:
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url: image-19.png
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- text: in the style of <s0><s1>
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output:
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url: image-20.png
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- text: in the style of <s0><s1>
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output:
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url: image-21.png
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- text: in the style of <s0><s1>
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output:
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url: image-22.png
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- text: in the style of <s0><s1>
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output:
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url: image-23.png
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- text: in the style of <s0><s1>
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output:
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- text: in the style of <s0><s1>
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output:
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url: image-25.png
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- text: in the style of <s0><s1>
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output:
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url: image-26.png
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base_model: stabilityai/stable-diffusion-xl-base-1.0
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instance_prompt: in the style of <s0><s1>
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license: openrail++
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---
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# SDXL LoRA DreamBooth - UNSAGroupe/unsalora
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<Gallery />
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## Model description
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### These are UNSAGroupe/unsalora LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
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## Download model
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### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
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- **LoRA**: download **[`unsalora.safetensors` here 💾](/UNSAGroupe/unsalora/blob/main/unsalora.safetensors)**.
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- Place it on your `models/Lora` folder.
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- On AUTOMATIC1111, load the LoRA by adding `<lora:unsalora: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 **[`unsalora_emb.safetensors` here 💾](/UNSAGroupe/unsalora/blob/main/unsalora_emb.safetensors)**.
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- Place it on it on your `embeddings` folder
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- Use it by adding `unsalora_emb` to your prompt. For example, `in the style of unsalora_emb`
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(you need both the LoRA and the embeddings as they were trained together for this LoRA)
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## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
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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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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('UNSAGroupe/unsalora', weight_name='pytorch_lora_weights.safetensors')
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embedding_path = hf_hub_download(repo_id='UNSAGroupe/unsalora', filename='unsalora_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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image = pipeline('in the style of <s0><s1>').images[0]
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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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## Trigger words
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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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to trigger concept `TOK` → use `<s0><s1>` in your prompt
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## Details
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All [Files & versions](/UNSAGroupe/unsalora/tree/main).
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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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LoRA for the text encoder was enabled. False.
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Pivotal tuning was enabled: True.
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Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
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logs/dreambooth-lora-sd-xl/1729585266.1896975/events.out.tfevents.1729585266.r-unsagroupe-autotrain-unsalora-e833dgoe-48c16-6nlkx.212.1
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version https://git-lfs.github.com/spec/v1
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oid sha256:60d2783c87ccd6f2640afb180013149bb172d648a3a6388e8ff14a47682e3758
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size 3571
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logs/dreambooth-lora-sd-xl/1729585266.1909153/hparams.yml
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adam_beta1: 0.9
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adam_beta2: 0.999
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adam_epsilon: 1.0e-08
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adam_weight_decay: 0.0001
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adam_weight_decay_text_encoder: null
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allow_tf32: false
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cache_dir: null
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cache_latents: true
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caption_column: prompt
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center_crop: false
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checkpointing_steps: 100000
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checkpoints_total_limit: null
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class_data_dir: null
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class_prompt: null
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crops_coords_top_left_h: 0
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crops_coords_top_left_w: 0
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dataloader_num_workers: 0
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dataset_config_name: null
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dataset_name: ./c50c0e6c-e210-4fe5-839a-5d6959cb1cb8
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enable_xformers_memory_efficient_attention: false
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gradient_accumulation_steps: 1
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gradient_checkpointing: true
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hub_model_id: null
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hub_token: null
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image_column: image
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instance_data_dir: null
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instance_prompt: in the style of <s0><s1>
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learning_rate: 1.0
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local_rank: -1
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logging_dir: logs
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lr_num_cycles: 1
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lr_power: 1.0
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lr_scheduler: constant
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lr_warmup_steps: 0
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max_grad_norm: 1.0
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max_train_steps: 1500
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mixed_precision: bf16
|
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