Upload folder using huggingface_hub

README.md

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+---
+tags:
+- text-to-image
+- flux
+- lora
+- diffusers
+- template:sd-lora
+- fluxgym
+
+
+base_model: black-forest-labs/FLUX.1-dev
+instance_prompt: prci
+license: other
+license_name: flux-1-dev-non-commercial-license
+license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
+---
+
+# photorealistic-car-images
+
+A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
+
+<Gallery />
+
+## Trigger words
+
+You should use `prci` to trigger the image generation.
+
+## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
+
+Weights for this model are available in Safetensors format.
+

dataset.toml

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+[general]
+shuffle_caption = false
+caption_extension = '.txt'
+keep_tokens = 1
+
+[[datasets]]
+resolution = 768
+batch_size = 1
+keep_tokens = 1
+
+  [[datasets.subsets]]
+  image_dir = 'D:\MISC\pinokio\api\fluxgym.git\datasets\photorealistic-car-images'
+  class_tokens = 'prci'
+  num_repeats = 6

photorealistic-car-images-000004.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6a17825614eb6b112ff4e00a959dc2ea23db1a3616f97e6b912f2cb277190c40
+size 158646024

photorealistic-car-images.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8e1911cc2573fdd8f668d347771a3cce749ca57823a77e23f20891012b3a9895
+size 158646024

train.bat

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+accelerate launch ^
+  --mixed_precision bf16 ^
+  --num_cpu_threads_per_process 1 ^
+  sd-scripts/flux_train_network.py ^
+  --pretrained_model_name_or_path "D:\MISC\pinokio\api\fluxgym.git\models\unet\bdsqlsz\flux1-dev2pro-single\flux1-dev2pro.safetensors" ^
+  --clip_l "D:\MISC\pinokio\api\fluxgym.git\models\clip\clip_l.safetensors" ^
+  --t5xxl "D:\MISC\pinokio\api\fluxgym.git\models\clip\t5xxl_fp16.safetensors" ^
+  --ae "D:\MISC\pinokio\api\fluxgym.git\models\vae\ae.sft" ^
+  --cache_latents_to_disk ^
+  --save_model_as safetensors ^
+  --sdpa --persistent_data_loader_workers ^
+  --max_data_loader_n_workers 2 ^
+  --seed 42 ^
+  --gradient_checkpointing ^
+  --mixed_precision bf16 ^
+  --save_precision bf16 ^
+  --network_module networks.lora_flux ^
+  --network_dim 16 ^
+  --optimizer_type adafactor ^
+  --optimizer_args "relative_step=False" "scale_parameter=False" "warmup_init=False" ^
+  --lr_scheduler constant_with_warmup ^
+  --max_grad_norm 0.0 ^
+  --learning_rate 8e-4 ^
+  --cache_text_encoder_outputs ^
+  --cache_text_encoder_outputs_to_disk ^
+  --fp8_base ^
+  --highvram ^
+  --max_train_epochs 7 ^
+  --save_every_n_epochs 4 ^
+  --dataset_config "D:\MISC\pinokio\api\fluxgym.git\outputs\photorealistic-car-images\dataset.toml" ^
+  --output_dir "D:\MISC\pinokio\api\fluxgym.git\outputs\photorealistic-car-images" ^
+  --output_name photorealistic-car-images ^
+  --timestep_sampling shift ^
+  --discrete_flow_shift 3.1582 ^
+  --model_prediction_type raw ^
+  --guidance_scale 1 ^
+  --loss_type l2 ^

uploader.py

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+from huggingface_hub import HfApi, login
+import os
+import time
+import sys
+
+def upload_to_huggingface(repo_name, wait_minutes):
+    # Convert minutes to seconds and wait
+    print(f"Script will upload files in {wait_minutes} minutes...")
+    time.sleep(wait_minutes * 60)
+    
+    # Initialize Hugging Face API
+    api = HfApi()
+    
+    try:
+        # Authenticate using your Hugging Face API token
+        hf_token = os.getenv("HF_API_TOKEN")
+        if not hf_token:
+            raise ValueError("Hugging Face API token is not set. Please set it in the environment variable 'HF_API_TOKEN'.")
+        login(token=hf_token)
+        
+        api.create_repo(repo_id=repo_name, private=True, exist_ok=True)
+
+        # Get current directory
+        current_dir = os.getcwd()
+        
+        print("Starting upload to Hugging Face...")
+        
+        # Upload all files from current directory
+        api.upload_folder(
+            folder_path=current_dir,
+            repo_id=repo_name,
+            repo_type="model"  
+        )
+        
+        print("Upload completed successfully!")
+        
+    except Exception as e:
+        print(f"An error occurred: {str(e)}")
+
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        print("Usage: python script.py <repository_name> <minutes_to_wait>")
+        print("Example: python script.py 'your-username/model-name' 120")
+        sys.exit(1)
+    
+    repo_name = sys.argv[1]
+    wait_minutes = int(sys.argv[2])
+    
+    upload_to_huggingface(repo_name, wait_minutes)