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models/people_Carlos_Menem_munba_200/README.md

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+---
+hyperparameters:
+  lora_r: 16
+  lora_alpha: 4
+  is_lora_negated: true
+  seed: 42
+  model_name_or_path: CompVis/stable-diffusion-v1-4
+  revision: null
+  variant: null
+  dataset_forget_name: assets/datasets/lfw_splits_filtered/Carlos_Menem/train_forget
+  dataset_retain_name: assets/datasets/lfw_splits_filtered/Carlos_Menem/train_retain
+  dataset_forget_config_name: null
+  dataset_retain_config_name: null
+  image_column: image
+  caption_column: text
+  validation_prompt: An image of Carlos Menem
+  num_validation_images: 1
+  validation_epochs: 201
+  resolution: 512
+  center_crop: false
+  random_flip: true
+  max_train_samples: null
+  dataloader_num_workers: 2
+  prediction_type: null
+  per_device_train_batch_size: 2
+  gradient_accumulation_steps: 2
+  num_train_epochs: 200
+  learning_rate: 0.0006
+  lr_scheduler_type: constant
+  should_log: true
+  local_rank: -1
+  device: cuda
+  n_gpu: 1
+  output_dir: assets/models/people_Carlos_Menem_munba_200
+  cache_dir: null
+  hub_token: null
+  hub_model_id: null
+  logging_dir: logs
+  logging_steps: 20
+  save_strategy: epoch
+  save_total_limit: 2
+  gradient_checkpointing: false
+  enable_xformers_memory_efficient_attention: false
+  mixed_precision: 'no'
+  allow_tf32: false
+  use_8bit_adam: false
+  report_to: tensorboard
+  compute_gradient_conflict: false
+  compute_runtimes: true
+  compute_memory: true
+  max_train_steps: 400
+  lr_warmup_steps: 0
+  adam_beta1: 0.9
+  adam_beta2: 0.999
+  adam_weight_decay: 0.01
+  adam_epsilon: 1.0e-08
+  max_grad_norm: 5.0
+  checkpointing_steps: 10000
+  checkpoints_total_limit: null
+  resume_from_checkpoint: null
+  noise_offset: 0.0
+model-index:
+- name: None
+  results:
+  - task:
+      type: text-to-image
+    dataset:
+      name: Forget set
+      type: inline-prompts
+    metrics:
+    - type: clip
+      value: 33.64024114608765
+      name: ForgetSet clip score of original model mean (~↑)
+    - type: clip
+      value: 3.1557685733520335
+      name: ForgetSet clip score of original model std (~↓)
+    - type: clip
+      value: 21.1767315864563
+      name: ForgetSet clip score of learned model mean (~↑)
+    - type: clip
+      value: 0.5174281021036993
+      name: ForgetSet clip score of learned model std (~↓)
+    - type: clip
+      value: 24.02331829071045
+      name: ForgetSet clip score of unlearned model mean (↓)
+    - type: clip
+      value: 2.0135561976578567
+      name: ForgetSet clip score of unlearned model std (~↓)
+    - type: clip
+      value: -2.8465867042541504
+      name: ForgetSet clip score difference between learned and unlearned mean (↑)
+    - type: clip
+      value: 2.122567916641444
+      name: ForgetSet clip score difference between learned and unlearned std (~↓)
+    - type: clip
+      value: 9.616922855377197
+      name: ForgetSet clip score difference between original and unlearned mean (↑)
+    - type: clip
+      value: 5.128420930046243
+      name: ForgetSet clip score difference between original and unlearned std (~↓)
+    - type: clip
+      value: 12.463509559631348
+      name: ForgetSet clip score difference between original and learned mean (↓)
+    - type: clip
+      value: 3.0206327665120503
+      name: ForgetSet clip score difference between original and learned std (~↓)
+    - type: clip
+      value: 30.71055316925049
+      name: RetainSet clip score of original model mean (~↑)
+    - type: clip
+      value: 1.864312610848831
+      name: RetainSet clip score of original model std (~↓)
+    - type: clip
+      value: 22.2283353805542
+      name: RetainSet clip score of learned model mean (~↓)
+    - type: clip
+      value: 0.48952612071895446
+      name: RetainSet clip score of learned model std (~↓)
+    - type: clip
+      value: 27.552839279174805
+      name: RetainSet clip score of unlearned model mean (↑)
+    - type: clip
+      value: 4.069867281608924
+      name: RetainSet clip score of unlearned model std (~↓)
+    - type: clip
+      value: -5.3245038986206055
+      name: RetainSet clip score difference between learned and unlearned mean (↓)
+    - type: clip
+      value: 3.8257833841662636
+      name: RetainSet clip score difference between learned and unlearned std (~↓)
+    - type: clip
+      value: 3.1577138900756836
+      name: RetainSet clip score difference between original and unlearned mean (↓)
+    - type: clip
+      value: 3.5168765994475355
+      name: RetainSet clip score difference between original and unlearned std (~↓)
+    - type: clip
+      value: 8.482217788696289
+      name: RetainSet clip score difference between original and learned mean (↑)
+    - type: clip
+      value: 1.448862558004187
+      name: RetainSet clip score difference between original and learned std (~↓)
+    - type: runtime
+      value: 7.08506174882253
+      name: Inference latency seconds mean (↓)
+    - type: runtime
+      value: 0.1205052687084748
+      name: Inference latency seconds std (~↓)
+  - task:
+      type: text-to-image
+    dataset:
+      name: assets/datasets/lfw_splits_filtered/Carlos_Menem/train_forget (forget)
+        and assets/datasets/lfw_splits_filtered/Carlos_Menem/train_retain (retain)
+        sets
+      type: forget-and-retain-together
+    metrics:
+    - type: runtime
+      value: 3.787001132965088
+      name: Runtime init seconds (~↓)
+    - type: runtime
+      value: 30.71293592453003
+      name: Runtime data loading seconds (~↓)
+    - type: runtime
+      value: 1396.6229717731476
+      name: Runtime training seconds (↓)
+    - type: runtime
+      value: 182.4927041530609
+      name: Runtime eval seconds (~↓)
+    - type: memory
+      value: 0
+      name: Peak memory usage in training (~↓)
+---
+
+<!-- This model card has been generated automatically according to the information the training script had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+
+# LoRA text2image fine-tuning - None
+These are LoRA adaption weights for CompVis/stable-diffusion-v1-4.
+The weights were fine-tuned for forgetting assets/datasets/lfw_splits_filtered/Carlos_Menem/train_forget dataset, while retaining assets/datasets/lfw_splits_filtered/Carlos_Menem/train_retain.
+You can find some example images in the following.
+
+![img](images/val_prompt_00_01.png)
+![img](images/tst_prompt_199_01.png)
+![img](images/Forget - An image of Carlos Menem.png)
+![img](images/Forget - Photograph of Carlos Menem; high definition.png)
+![img](images/Forget - An picture of Carlos Menem in the rain.png)
+![img](images/Forget - An picture of Carlos Menem running.png)
+![img](images/Retain - An image of a child.png)
+![img](images/Retain - Photograph of a child; high definition.png)
+![img](images/Retain - An picture of a child in the rain.png)
+![img](images/Retain - An picture of a child running.png)
+
+
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+# TODO: add an example code snippet for running this diffusion pipeline
+```
+
+#### Limitations and bias
+
+[TODO: provide examples of latent issues and potential remediations]
+
+## Training details
+
+[TODO: describe the data used to train the model]

models/people_Carlos_Menem_munba_200/logs/text2image-fine-tune/1771450156.052087/events.out.tfevents.1771450156.erdos.cl.itk.ppke.hu.3328580.1

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models/people_Carlos_Menem_munba_200/logs/text2image-fine-tune/1771450156.0560246/hparams.yml

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+adam_beta1: 0.9
+adam_beta2: 0.999
+adam_epsilon: 1.0e-08
+adam_weight_decay: 0.01
+allow_tf32: false
+cache_dir: null
+caption_column: text
+center_crop: false
+checkpointing_steps: 10000
+checkpoints_total_limit: null
+compute_gradient_conflict: false
+compute_memory: true
+compute_runtimes: true
+dataloader_num_workers: 2
+dataset_forget_config_name: null
+dataset_forget_name: assets/datasets/lfw_splits_filtered/Carlos_Menem/train_forget
+dataset_retain_config_name: null
+dataset_retain_name: assets/datasets/lfw_splits_filtered/Carlos_Menem/train_retain
+device: cuda
+enable_xformers_memory_efficient_attention: false
+gradient_accumulation_steps: 2
+gradient_checkpointing: false
+hub_model_id: null
+hub_token: null
+image_column: image
+is_lora_negated: true
+learning_rate: 0.0006
+local_rank: -1
+logging_dir: logs
+logging_steps: 20
+lora_alpha: 4
+lora_r: 16
+lr_scheduler_type: constant
+lr_warmup_steps: 0
+max_grad_norm: 5.0
+max_train_samples: null
+max_train_steps: 400
+mixed_precision: 'no'
+model_name_or_path: CompVis/stable-diffusion-v1-4
+n_gpu: 1
+noise_offset: 0.0
+num_train_epochs: 200
+num_validation_images: 1
+output_dir: assets/models/people_Carlos_Menem_munba_200
+per_device_train_batch_size: 2
+prediction_type: null
+random_flip: true
+report_to: tensorboard
+resolution: 512
+resume_from_checkpoint: null
+revision: null
+save_strategy: epoch
+save_total_limit: 2
+seed: 42
+should_log: true
+use_8bit_adam: false
+validation_epochs: 201
+validation_prompt: An image of Carlos Menem
+variant: null

models/people_Carlos_Menem_munba_200/logs/text2image-fine-tune/events.out.tfevents.1771450156.erdos.cl.itk.ppke.hu.3328580.0

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models/people_Carlos_Menem_munba_200/pytorch_lora_weights.safetensors

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