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.gitattributes

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README.md

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+---
+tags:
+- stable-diffusion-xl
+- stable-diffusion-xl-diffusers
+- text-to-image
+- diffusers
+- lora
+- template:sd-lora
+widget:
+- text: <s0><s1>
+  output:
+    url: image-0.png
+- text: <s0><s1>
+  output:
+    url: image-1.png
+- text: <s0><s1>
+  output:
+    url: image-2.png
+- text: <s0><s1>
+  output:
+    url: image-3.png
+- text: <s0><s1>
+  output:
+    url: image-4.png
+- text: <s0><s1>
+  output:
+    url: image-5.png
+base_model: stabilityai/stable-diffusion-xl-base-1.0
+instance_prompt: <s0><s1>
+license: openrail++
+---
+
+# SDXL LoRA DreamBooth - multimodalart/poli-multiplier-100-face-token-only-pivot-75
+
+<Gallery />
+
+## Model description
+
+### These are multimodalart/poli-multiplier-100-face-token-only-pivot-75 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
+
+## Trigger words
+
+To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
+
+to trigger concept `TOK` → use `<s0><s1>` in your prompt 
+
+
+
+## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
+
+```py
+from diffusers import AutoPipelineForText2Image
+import torch
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+        
+pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
+pipeline.load_lora_weights('multimodalart/poli-multiplier-100-face-token-only-pivot-75', weight_name='pytorch_lora_weights.safetensors')
+embedding_path = hf_hub_download(repo_id='multimodalart/poli-multiplier-100-face-token-only-pivot-75', filename="embeddings.safetensors", repo_type="model")
+state_dict = load_file(embedding_path)
+pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
+pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
+        
+image = pipeline('<s0><s1>').images[0]
+```
+
+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)
+
+## Download model
+
+### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
+
+- Download the LoRA *.safetensors [here](/multimodalart/poli-multiplier-100-face-token-only-pivot-75/blob/main/pytorch_lora_weights.safetensors). Rename it and place it on your Lora folder.
+- Download the text embeddings *.safetensors [here](/multimodalart/poli-multiplier-100-face-token-only-pivot-75/blob/main/embeddings.safetensors). Rename it and place it on it on your embeddings folder.
+
+All [Files & versions](/multimodalart/poli-multiplier-100-face-token-only-pivot-75/tree/main).
+
+## Details
+
+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).
+
+LoRA for the text encoder was enabled. False.
+
+Pivotal tuning was enabled: True.
+
+Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
+

embeddings.safetensors

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+size 8344

logs/dreambooth-lora-sd-xl/1703077456.172351/events.out.tfevents.1703077456.r-multimodalart-autotrain-poli-multiplier-100-face-to-ed2dtl452.190.1

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logs/dreambooth-lora-sd-xl/1703077456.1743588/hparams.yml

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+adam_beta1: 0.9
+adam_beta2: 0.999
+adam_epsilon: 1.0e-08
+adam_weight_decay: 0.0001
+adam_weight_decay_text_encoder: 0.0
+allow_tf32: false
+cache_dir: null
+cache_latents: true
+caption_column: prompt
+center_crop: false
+checkpointing_steps: 5000
+checkpoints_total_limit: null
+class_data_dir: 92365f65-9c6b-4bad-86fc-25d6ee4a5183
+class_prompt: a photo of a person
+crops_coords_top_left_h: 0
+crops_coords_top_left_w: 0
+dataloader_num_workers: 0
+dataset_config_name: null
+dataset_name: ./3b2f9020-1161-47db-ab52-71e7ba86b3b3
+enable_xformers_memory_efficient_attention: false
+gradient_accumulation_steps: 1
+gradient_checkpointing: true
+hub_model_id: null
+hub_token: null
+image_column: image
+instance_data_dir: null
+instance_prompt: <s0><s1>
+learning_rate: 1.0
+local_rank: -1
+logging_dir: logs
+lr_num_cycles: 1
+lr_power: 1.0
+lr_scheduler: constant
+lr_warmup_steps: 0
+max_grad_norm: 1.0
+max_train_steps: 600
+mixed_precision: bf16
+num_class_images: 150
+num_new_tokens_per_abstraction: 2
+num_train_epochs: 8
+num_validation_images: 4
+optimizer: prodigy
+output_dir: poli-multiplier-100-face-token-only-pivot-75
+pretrained_model_name_or_path: stabilityai/stable-diffusion-xl-base-1.0
+pretrained_vae_model_name_or_path: madebyollin/sdxl-vae-fp16-fix
+prior_generation_precision: null
+prior_loss_weight: 1.0
+prodigy_beta3: 0.0
+prodigy_decouple: true
+prodigy_safeguard_warmup: true
+prodigy_use_bias_correction: true
+push_to_hub: false
+rank: 32
+repeats: 3
+report_to: tensorboard
+resolution: 1024
+resume_from_checkpoint: null
+revision: null
+sample_batch_size: 4
+scale_lr: false
+seed: 42
+snr_gamma: null
+text_encoder_lr: 1.0
+token_abstraction: TOK
+train_batch_size: 2
+train_text_encoder: false
+train_text_encoder_frac: 1.0
+train_text_encoder_ti: true
+train_text_encoder_ti_frac: 0.75
+use_8bit_adam: false
+validation_epochs: 50
+validation_prompt: null
+variant: null
+with_prior_preservation: true

logs/dreambooth-lora-sd-xl/events.out.tfevents.1703077456.r-multimodalart-autotrain-poli-multiplier-100-face-to-ed2dtl452.190.0

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pytorch_lora_weights.safetensors

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