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trainFlux/download_caption_flux.py

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+from datasets import load_dataset
+import os
+import shutil
+
+# 加載 HuggingFace 數據集
+
+dataset = load_dataset("housearch/Park-PFI-Overhead")
+
+# 創建目錄
+output_dir = "/workspace/datasets/"
+os.makedirs(output_dir, exist_ok=True)
+
+# 下載並保存圖像和標題
+for i, item in enumerate(dataset["train"]):
+	# 保存圖像
+	image = item["image"]
+	image = image.convert("RGB")
+	image_path = os.path.join(output_dir, f"image_{i:06d}.jpg")
+	image.save(image_path)
+	
+	# 保存標題
+	caption = item["caption_Flux"]
+	caption_path = os.path.join(output_dir, f"image_{i:06d}.txt")
+	with open(caption_path, "w", encoding="utf-8") as f:
+		f.write(caption)

trainFlux/landscapeOverhead_A6000.json

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+{
+  "LoRA_type": "Flux1",
+  "LyCORIS_preset": "full",
+  "adaptive_noise_scale": 0,
+  "additional_parameters": "",
+  "ae": "/workspace/flux_vae.safetensors",
+  "apply_t5_attn_mask": false,
+  "async_upload": false,
+  "block_alphas": "",
+  "block_dims": "",
+  "block_lr_zero_threshold": "",
+  "blocks_to_swap": 0,
+  "bucket_no_upscale": true,
+  "bucket_reso_steps": 64,
+  "bypass_mode": false,
+  "cache_latents": false,
+  "cache_latents_to_disk": true,
+  "caption_dropout_every_n_epochs": 0,
+  "caption_dropout_rate": 0,
+  "caption_extension": ".txt",
+  "clip_g": "",
+  "clip_g_dropout_rate": 0,
+  "clip_l": "/workspace/clip_l.safetensors",
+  "clip_skip": 1,
+  "color_aug": false,
+  "constrain": 0,
+  "conv_alpha": 1,
+  "conv_block_alphas": "",
+  "conv_block_dims": "",
+  "conv_dim": 1,
+  "cpu_offload_checkpointing": false,
+  "dataset_config": "",
+  "debiased_estimation_loss": false,
+  "decompose_both": false,
+  "dim_from_weights": false,
+  "discrete_flow_shift": 3,
+  "dora_wd": false,
+  "double_blocks_to_swap": 0,
+  "down_lr_weight": "",
+  "dynamo_backend": "no",
+  "dynamo_mode": "default",
+  "dynamo_use_dynamic": false,
+  "dynamo_use_fullgraph": false,
+  "enable_all_linear": false,
+  "enable_bucket": true,
+  "epoch": 10,
+  "extra_accelerate_launch_args": "",
+  "factor": -1,
+  "flip_aug": false,
+  "flux1_cache_text_encoder_outputs": false,
+  "flux1_cache_text_encoder_outputs_to_disk": true,
+  "flux1_checkbox": true,
+  "fp8_base": false,
+  "fp8_base_unet": false,
+  "full_bf16": true,
+  "full_fp16": false,
+  "gpu_ids": "",
+  "gradient_accumulation_steps": 1,
+  "gradient_checkpointing": true,
+  "guidance_scale": 3.5,
+  "highvram": false,
+  "huber_c": 0.1,
+  "huber_scale": 1,
+  "huber_schedule": "snr",
+  "huggingface_path_in_repo": "",
+  "huggingface_repo_id": "",
+  "huggingface_repo_type": "",
+  "huggingface_repo_visibility": "",
+  "huggingface_token": "",
+  "img_attn_dim": "",
+  "img_mlp_dim": "",
+  "img_mod_dim": "",
+  "in_dims": "",
+  "ip_noise_gamma": 0,
+  "ip_noise_gamma_random_strength": false,
+  "keep_tokens": 0,
+  "learning_rate": 1e-05,
+  "log_config": false,
+  "log_tracker_config": "",
+  "log_tracker_name": "",
+  "log_with": "",
+  "logging_dir": "/workspace/train/log",
+  "logit_mean": 0,
+  "logit_std": 1,
+  "loraplus_lr_ratio": 0,
+  "loraplus_text_encoder_lr_ratio": 0,
+  "loraplus_unet_lr_ratio": 0,
+  "loss_type": "l2",
+  "lowvram": false,
+  "lr_scheduler": "cosine_with_restarts",
+  "lr_scheduler_args": "",
+  "lr_scheduler_num_cycles": 1,
+  "lr_scheduler_power": 1,
+  "lr_scheduler_type": "",
+  "lr_warmup": 10,
+  "lr_warmup_steps": 0,
+  "main_process_port": 0,
+  "masked_loss": false,
+  "max_bucket_reso": 2048,
+  "max_data_loader_n_workers": 0,
+  "max_grad_norm": 1,
+  "max_resolution": "1024,1024",
+  "max_timestep": 1000,
+  "max_token_length": 225,
+  "max_train_epochs": 0,
+  "max_train_steps": 0,
+  "mem_eff_attn": false,
+  "mem_eff_save": false,
+  "metadata_author": "Chris Hsu",
+  "metadata_description": "landscape for Overhead view",
+  "metadata_license": "",
+  "metadata_tags": "",
+  "metadata_title": "landscapeOverhead_A6000",
+  "mid_lr_weight": "",
+  "min_bucket_reso": 256,
+  "min_snr_gamma": 0,
+  "min_timestep": 0,
+  "mixed_precision": "bf16",
+  "mode_scale": 1.29,
+  "model_list": "custom",
+  "model_prediction_type": "sigma_scaled",
+  "module_dropout": 0,
+  "multi_gpu": false,
+  "multires_noise_discount": 0.3,
+  "multires_noise_iterations": 0,
+  "network_alpha": 8,
+  "network_dim": 4,
+  "network_dropout": 0,
+  "network_weights": "",
+  "noise_offset": 0,
+  "noise_offset_random_strength": false,
+  "noise_offset_type": "Original",
+  "num_cpu_threads_per_process": 2,
+  "num_machines": 1,
+  "num_processes": 1,
+  "optimizer": "AdamW8bit",
+  "optimizer_args": "",
+  "output_dir": "/workspace/train/model",
+  "output_name": "landscapeOverhead_A6000",
+  "persistent_data_loader_workers": false,
+  "pos_emb_random_crop_rate": 0,
+  "pretrained_model_name_or_path": "/workspace/flux1-dev.safetensors",
+  "prior_loss_weight": 1,
+  "random_crop": false,
+  "rank_dropout": 0,
+  "rank_dropout_scale": false,
+  "reg_data_dir": "",
+  "rescaled": false,
+  "resume": "",
+  "resume_from_huggingface": "",
+  "sample_every_n_epochs": 1,
+  "sample_every_n_steps": 0,
+  "sample_prompts": "Detailed aerial watercolor rendering of a comprehensive landscape architecture master plan for an urban park development. The illustration showcases a minimalist curved cultural building with a distinctive concave gray roof as the focal architectural element, strategically positioned in relation to surrounding green spaces. The landscape design features an intricate network of wooden boardwalks and earth-toned pathways that create a flowing circulation system throughout the park, connecting various programmatic zones. --w 1024 --h 768 --l 7.0 \nA sophisticated aerial perspective visualization illustrating an urban park that exemplifies contemporary landscape architectural principles through its celebration of seasonal phenomena, particularly the ephemeral beauty of flowering cherry trees. The rendering employs a refined watercolor technique with nuanced transparent washes that create atmospheric depth while maintaining clarity of spatial relationships, particularly evident in the treatment of vegetation masses and the subtle modulation of green tones throughout the composition. --w 1024 --h 768 --l 7.0\nA comprehensive bird's-eye perspective visualization illustrating an ambitious waterfront park development that exemplifies contemporary landscape architectural principles through its integration of spectacular ornamental displays, varied recreational spaces, and thoughtful visitor amenities. The illustration employs a vibrant watercolor technique with saturated color application that emphasizes the park's dramatic flowering meadows while maintaining clarity of spatial relationships. --w 1024 --h 768 --l 7.0",
+  "sample_sampler": "euler_a",
+  "save_as_bool": false,
+  "save_clip": false,
+  "save_every_n_epochs": 1,
+  "save_every_n_steps": 0,
+  "save_last_n_epochs": 0,
+  "save_last_n_epochs_state": 0,
+  "save_last_n_steps": 0,
+  "save_last_n_steps_state": 0,
+  "save_model_as": "safetensors",
+  "save_precision": "bf16",
+  "save_state": false,
+  "save_state_on_train_end": false,
+  "save_state_to_huggingface": false,
+  "save_t5xxl": false,
+  "scale_v_pred_loss_like_noise_pred": false,
+  "scale_weight_norms": 0,
+  "sd3_cache_text_encoder_outputs": false,
+  "sd3_cache_text_encoder_outputs_to_disk": false,
+  "sd3_checkbox": false,
+  "sd3_clip_l": "",
+  "sd3_clip_l_dropout_rate": 0,
+  "sd3_disable_mmap_load_safetensors": false,
+  "sd3_enable_scaled_pos_embed": false,
+  "sd3_fused_backward_pass": false,
+  "sd3_t5_dropout_rate": 0,
+  "sd3_t5xxl": "",
+  "sd3_text_encoder_batch_size": 1,
+  "sdxl": false,
+  "sdxl_cache_text_encoder_outputs": false,
+  "sdxl_no_half_vae": false,
+  "seed": 0,
+  "shuffle_caption": false,
+  "single_blocks_to_swap": 0,
+  "single_dim": "",
+  "single_mod_dim": "",
+  "skip_cache_check": false,
+  "split_mode": false,
+  "split_qkv": false,
+  "stop_text_encoder_training": 0,
+  "t5xxl": "/workspace/t5xxl_fp16.safetensors",
+  "t5xxl_device": "",
+  "t5xxl_dtype": "bf16",
+  "t5xxl_lr": 1e-05,
+  "t5xxl_max_token_length": 512,
+  "text_encoder_lr": 1e-05,
+  "timestep_sampling": "sigma",
+  "train_batch_size": 2,
+  "train_blocks": "all",
+  "train_data_dir": "/workspace/train/img",
+  "train_double_block_indices": "all",
+  "train_norm": false,
+  "train_on_input": true,
+  "train_single_block_indices": "all",
+  "train_t5xxl": false,
+  "training_comment": "",
+  "txt_attn_dim": "",
+  "txt_mlp_dim": "",
+  "txt_mod_dim": "",
+  "unet_lr": 1e-05,
+  "unit": 1,
+  "up_lr_weight": "",
+  "use_cp": false,
+  "use_scalar": false,
+  "use_tucker": false,
+  "v2": false,
+  "v_parameterization": false,
+  "v_pred_like_loss": 0,
+  "vae": "",
+  "vae_batch_size": 0,
+  "wandb_api_key": "",
+  "wandb_run_name": "",
+  "weighted_captions": false,
+  "weighting_scheme": "logit_normal",
+  "xformers": "xformers"
+}

trainFlux/trainFlux.sh

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+#使用 bash指令執行這個腳本 ---> bash trainFlux.sh  
+#!/bin/bash
+cd /workspace
+python -m venv env 
+source /workspace/env/bin/activate
+pip install datasets huggingface_hub Pillow
+python download_caption_flux.py
+deactivate
+cd /workspace
+git clone https://github.com/bmaltais/kohya_ss
+cd kohya_ss
+#預設是訓練SD1.5, SDXL的版本，如果要訓練SD3, Flux 必須切換至sd3-flux.1
+git branch -a
+git checkout sd3-flux.1
+./setup-runpod.sh
+./gui.sh --share --headless
+
+'''
+另開 terminal 視窗，把以下內容貼上並按下enter執行，直接下載訓練Flux LoRA必須的相關模型(節省時間)
+
+wget --trust-server-name --content-disposition  https://huggingface.co/realung/flux1-dev.safetensors/resolve/main/flux1-dev.safetensors
+
+wget --trust-server-name --content-disposition https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/t5xxl_fp16.safetensors
+
+wget --trust-server-name --content-disposition https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/clip_l.safetensors
+
+wget --trust-server-name --content-disposition https://huggingface.co/StableDiffusionVN/Flux/resolve/main/Vae/flux_vae.safetensors
+'''
+#./gui.sh --share --headless --config "/workspace/config.toml"