End of training

README.md

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+---
+base_model: CompVis/stable-diffusion-v1-4
+library_name: diffusers
+license: creativeml-openrail-m
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- text-to-image
+- diffusers
+- diffusers-training
+inference: true
+---
+
+<!-- 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. -->
+
+
+# Text-to-image finetuning - haorandai/New_Vehicle_20Samples_epsilon_0.05_alpha_0.01_With20Constraints
+
+This pipeline was finetuned from **CompVis/stable-diffusion-v1-4** on the **haorandai/New_Vehicle_20Samples_epsilon_0.05_alpha_0.01_With20Constraints** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: None: 
+
+
+
+## Pipeline usage
+
+You can use the pipeline like so:
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+pipeline = DiffusionPipeline.from_pretrained("haorandai/New_Vehicle_20Samples_epsilon_0.05_alpha_0.01_With20Constraints", torch_dtype=torch.float16)
+prompt = "None"
+image = pipeline(prompt).images[0]
+image.save("my_image.png")
+```
+
+## Training info
+
+These are the key hyperparameters used during training:
+
+* Epochs: 20
+* Learning rate: 1e-05
+* Batch size: 1
+* Gradient accumulation steps: 4
+* Image resolution: 224
+* Mixed-precision: fp16
+
+
+
+## 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]

feature_extractor/preprocessor_config.json

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+    "height": 224,
+    "width": 224
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+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
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+  "do_resize": true,
+  "image_mean": [
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+    0.4578275,
+    0.40821073
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+  "image_processor_type": "CLIPImageProcessor",
+  "image_std": [
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+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 224
+  }
+}

logs/text2image-fine-tune/1727980984.8476117/events.out.tfevents.1727980984.datasec.engr.uconn.edu.25135.1

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logs/text2image-fine-tune/1727980984.8486567/hparams.yml

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+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: true
+checkpointing_steps: 500
+checkpoints_total_limit: null
+dataloader_num_workers: 0
+dataset_config_name: null
+dataset_name: haorandai/New_Vehicle_20Samples_epsilon_0.05_alpha_0.01_With20Constraints
+dream_detail_preservation: 1.0
+dream_training: false
+enable_xformers_memory_efficient_attention: false
+foreach_ema: false
+gradient_accumulation_steps: 4
+gradient_checkpointing: true
+hub_model_id: null
+hub_token: null
+image_column: image
+input_perturbation: 0
+learning_rate: 1.0e-05
+local_rank: -1
+logging_dir: logs
+lr_scheduler: constant
+lr_warmup_steps: 500
+max_grad_norm: 1.0
+max_train_samples: null
+max_train_steps: 200
+mixed_precision: fp16
+noise_offset: 0
+non_ema_revision: null
+num_train_epochs: 20
+offload_ema: false
+output_dir: /mnt/nvme1n1p1/home/biw23007/haoran/new_finetune_model_dir_1e-05_200_withconstraint/haorandai/New_Vehicle_20Samples_epsilon_0.05_alpha_0.01_With20Constraints/
+prediction_type: null
+pretrained_model_name_or_path: CompVis/stable-diffusion-v1-4
+push_to_hub: true
+random_flip: true
+report_to: tensorboard
+resolution: 224
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+tracker_project_name: text2image-fine-tune
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+validation_epochs: 5
+variant: null

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model_index.json

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+  "_class_name": "StableDiffusionPipeline",
+  "_diffusers_version": "0.31.0.dev0",
+  "_name_or_path": "CompVis/stable-diffusion-v1-4",
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+  "scheduler": [
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+  "vae": [
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+}

safety_checker/config.json

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scheduler/scheduler_config.json

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+  "num_train_timesteps": 1000,
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+  "skip_prk_steps": true,
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+  "timestep_spacing": "leading",
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text_encoder/config.json

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tokenizer/special_tokens_map.json

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