Update README.md

README.md

@@ -11,9 +11,10 @@ tags:
 - vintage
 - postcard
 ---
-# Wish You Were Here - a 1.5 LORA for vintage postcard replication
+# Wish You Were Here - a Stable diffusion 1.5 LORA for vintage postcard replication
 
 <!-- Provide a quick summary of what the model is/does. -->
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6537927953b7eb25ce03c962/d97rlp7IYnBcKPYQuCpBi.png)
 
 Wish you were here is a LORA model developped to create vintage postcard images. The model was trained on Stable Diffusion 1.5.
 
@@ -41,11 +42,19 @@ destinations. The model will also replicate damage seen in the images.
 
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+To use the WYWH model, use your favorite Stable Diffusion model (the recommended model is a realistic model) and use the LORA along with the following triggers:
+- WYWH (the base trigger)
+- Photograph (for photography postcards)
+- Drawing (for drawn postcards)
+- Damage (to add scratch and water damage to the generation)
+- Monochrome (for black and white images)
+
+For negatives, your can use the following:
+- White border (if you do not want a white border)
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+You can use this model with [automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui), [comfyui](https://github.com/comfyanonymous/ComfyUI) and [sdnext](https://github.com/vladmandic/automatic). 
 
 [More Information Needed]
 
@@ -54,23 +63,128 @@ Use the code below to get started with the model.
 ### Training Data
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-## Technical Specifications [optional]
+The Wish You Were Here dataset consists out of ~650 images of postcards from 1900-1970.
+Dataset: [origional dataset](https://huggingface.co/datasets/calm-and-collected/wish_you_were_here "The wish you were here dataset").
+
+### Training Hyperparameters
+
+<details>
+  <summary>Kohya_SS paramaters</summary>
+  ```js
+{
+  "LoRA_type": "Standard",
+  "adaptive_noise_scale": 0,
+  "additional_parameters": "",
+  "block_alphas": "",
+  "block_dims": "",
+  "block_lr_zero_threshold": "",
+  "bucket_no_upscale": true,
+  "bucket_reso_steps": 64,
+  "cache_latents": true,
+  "cache_latents_to_disk": true,
+  "caption_dropout_every_n_epochs": 0.0,
+  "caption_dropout_rate": 0,
+  "caption_extension": ".txt",
+  "clip_skip": 2,
+  "color_aug": false,
+  "conv_alpha": 1,
+  "conv_block_alphas": "",
+  "conv_block_dims": "",
+  "conv_dim": 1,
+  "decompose_both": false,
+  "dim_from_weights": false,
+  "down_lr_weight": "",
+  "enable_bucket": true,
+  "epoch": 1,
+  "factor": -1,
+  "flip_aug": false,
+  "full_bf16": false,
+  "full_fp16": false,
+  "gradient_accumulation_steps": 1,
+  "gradient_checkpointing": false,
+  "keep_tokens": "0",
+  "learning_rate": 0.0001,
+  "logging_dir": "/home/glow/Desktop/ml/whyw_logs",
+  "lora_network_weights": "",
+  "lr_scheduler": "constant",
+  "lr_scheduler_args": "",
+  "lr_scheduler_num_cycles": "",
+  "lr_scheduler_power": "",
+  "lr_warmup": 0,
+  "max_bucket_reso": 2048,
+  "max_data_loader_n_workers": "1",
+  "max_resolution": "512,650",
+  "max_timestep": 1000,
+  "max_token_length": "75",
+  "max_train_epochs": "100",
+  "max_train_steps": "",
+  "mem_eff_attn": true,
+  "mid_lr_weight": "",
+  "min_bucket_reso": 256,
+  "min_snr_gamma": 0,
+  "min_timestep": 0,
+  "mixed_precision": "bf16",
+  "model_list": "custom",
+  "module_dropout": 0.2,
+  "multires_noise_discount": 0.2,
+  "multires_noise_iterations": 8,
+  "network_alpha": 128,
+  "network_dim": 256,
+  "network_dropout": 0.3,
+  "no_token_padding": false,
+  "noise_offset": "0.05",
+  "noise_offset_type": "Multires",
+  "num_cpu_threads_per_process": 2,
+  "optimizer": "AdamW8bit",
+  "optimizer_args": "",
+  "output_dir": "/home/glow/Desktop/ml/whyw_logs/model_v2",
+  "output_name": "final_model",
+  "persistent_data_loader_workers": false,
+  "pretrained_model_name_or_path": "runwayml/stable-diffusion-v1-5",
+  "prior_loss_weight": 1.0,
+  "random_crop": false,
+  "rank_dropout": 0.2,
+  "reg_data_dir": "",
+  "resume": "",
+  "sample_every_n_epochs": 0,
+  "sample_every_n_steps": 0,
+  "sample_prompts": "",
+  "sample_sampler": "euler_a",
+  "save_every_n_epochs": 1,
+  "save_every_n_steps": 0,
+  "save_last_n_steps": 0,
+  "save_last_n_steps_state": 0,
+  "save_model_as": "safetensors",
+  "save_precision": "bf16",
+  "save_state": false,
+  "scale_v_pred_loss_like_noise_pred": false,
+  "scale_weight_norms": 1,
+  "sdxl": false,
+  "sdxl_cache_text_encoder_outputs": false,
+  "sdxl_no_half_vae": true,
+  "seed": "1234",
+  "shuffle_caption": false,
+  "stop_text_encoder_training": 1,
+  "text_encoder_lr": 5e-05,
+  "train_batch_size": 3,
+  "train_data_dir": "/home/glow/Desktop/wyhw",
+  "train_on_input": true,
+  "training_comment": "",
+  "unet_lr": 0.0001,
+  "unit": 1,
+  "up_lr_weight": "",
+  "use_cp": true,
+  "use_wandb": false,
+  "v2": false,
+  "v_parameterization": false,
+  "v_pred_like_loss": 0,
+  "vae_batch_size": 0,
+  "wandb_api_key": "",
+  "weighted_captions": false,
+  "xformers": "xformers"
+}
+  ```
+</details>
 
 #### Hardware
 
@@ -80,10 +194,6 @@ The model was trained on two GTX 4090 for a duration of 2 days to extract 100 ep
 
 The model was trained via the Kohya_SS gui.
 
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
 ## Model Card Contact
 
 Use the community section of this repository to contact me.