Commit
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Parent(s):
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End of training
Browse files- README.md +69 -0
- embeddings.safetensors +3 -0
- logs/dreambooth-lora-sd-xl/1701948557.9207277/events.out.tfevents.1701948557.r-multimodalart-autotrain-apolinariozito-3-0zn9vcb6-48b5f-jz767.262.1 +3 -0
- logs/dreambooth-lora-sd-xl/1701948557.9224653/hparams.yml +74 -0
- logs/dreambooth-lora-sd-xl/events.out.tfevents.1701948557.r-multimodalart-autotrain-apolinariozito-3-0zn9vcb6-48b5f-jz767.262.0 +3 -0
- pytorch_lora_weights.safetensors +3 -0
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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base_model: stabilityai/stable-diffusion-xl-base-1.0
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instance_prompt: A photo of <s0><s1>
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license: openrail++
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---
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# SDXL LoRA DreamBooth - multimodalart/apolinariozito-3
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<Gallery />
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## Model description
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### These are multimodalart/apolinariozito-3 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
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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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## 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('multimodalart/apolinariozito-3', weight_name='pytorch_lora_weights.safetensors')
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embedding_path = hf_hub_download(repo_id='multimodalart/apolinariozito-3', filename="embeddings.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=pipe.text_encoder, tokenizer=pipe.tokenizer)
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pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
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image = pipeline('A photo 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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## Download model
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### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
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- Download the LoRA *.safetensors [here](/multimodalart/apolinariozito-3/blob/main/pytorch_lora_weights.safetensors). Rename it and place it on your Lora folder.
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- Download the text embeddings *.safetensors [here](/multimodalart/apolinariozito-3/blob/main/embeddings.safetensors). Rename it and place it on it on your embeddings folder.
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All [Files & versions](/multimodalart/apolinariozito-3/tree/main).
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## Details
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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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embeddings.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:20bd8c6a03f7c8928d5590062789bf9cf45c479306009790c1670849c2e9e8c1
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size 8344
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logs/dreambooth-lora-sd-xl/1701948557.9207277/events.out.tfevents.1701948557.r-multimodalart-autotrain-apolinariozito-3-0zn9vcb6-48b5f-jz767.262.1
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version https://git-lfs.github.com/spec/v1
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oid sha256:c4298a37d672e0dc31794fa22634b8dd02cccc9ffa0fce2cc35ccbb265f49e32
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size 3792
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logs/dreambooth-lora-sd-xl/1701948557.9224653/hparams.yml
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adam_beta1: 0.9
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adam_beta2: 0.99
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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: 0.0
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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: 5000
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checkpoints_total_limit: null
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class_data_dir: d717c8f0-7125-445f-a405-645d96cba044
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class_prompt: a photo of a person
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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: ./22dd0893-a95f-4876-9f5d-c970db7ab78a
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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: A photo 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: 800
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mixed_precision: bf16
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num_class_images: 150
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num_new_tokens_per_abstraction: 2
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num_train_epochs: 11
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num_validation_images: 4
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optimizer: prodigy
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output_dir: apolinariozito-3
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pretrained_model_name_or_path: stabilityai/stable-diffusion-xl-base-1.0
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pretrained_vae_model_name_or_path: madebyollin/sdxl-vae-fp16-fix
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prior_generation_precision: null
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prior_loss_weight: 1.0
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prodigy_beta3: 0.0
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prodigy_decouple: true
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prodigy_safeguard_warmup: true
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prodigy_use_bias_correction: true
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push_to_hub: true
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rank: 64
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repeats: 3
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report_to: tensorboard
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resolution: 1024
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resume_from_checkpoint: null
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revision: null
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sample_batch_size: 4
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scale_lr: false
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seed: 42
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snr_gamma: null
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text_encoder_lr: 1.0
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token_abstraction: TOK
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train_batch_size: 2
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train_text_encoder: false
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train_text_encoder_frac: 1.0
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train_text_encoder_ti: true
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train_text_encoder_ti_frac: 0.5
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use_8bit_adam: false
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validation_epochs: 50
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validation_prompt: null
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variant: null
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with_prior_preservation: true
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logs/dreambooth-lora-sd-xl/events.out.tfevents.1701948557.r-multimodalart-autotrain-apolinariozito-3-0zn9vcb6-48b5f-jz767.262.0
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version https://git-lfs.github.com/spec/v1
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size 67034
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pytorch_lora_weights.safetensors
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version https://git-lfs.github.com/spec/v1
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size 371758976
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