mirror of
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diffsynth 2.0 prototype
This commit is contained in:
Artiprocher
@@ -1,12 +0,0 @@
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accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \
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--dataset_base_path data/example_image_dataset \
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--dataset_metadata_path data/example_image_dataset/metadata.csv \
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--max_pixels 1048576 \
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--dataset_repeat 200 \
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--model_id_with_origin_paths "ostris/Flex.2-preview:Flex.2-preview.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \
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--learning_rate 1e-5 \
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--num_epochs 1 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/FLEX.2-preview_full" \
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--trainable_models "dit" \
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--use_gradient_checkpointing
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@@ -1,14 +0,0 @@
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accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \
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--dataset_base_path data/example_image_dataset \
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--dataset_metadata_path data/example_image_dataset/metadata_kontext.csv \
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--data_file_keys "image,kontext_images" \
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--max_pixels 1048576 \
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--dataset_repeat 400 \
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--model_id_with_origin_paths "black-forest-labs/FLUX.1-Kontext-dev:flux1-kontext-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \
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--learning_rate 1e-5 \
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--num_epochs 1 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/FLUX.1-Kontext-dev_full" \
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--trainable_models "dit" \
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--extra_inputs "kontext_images" \
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--use_gradient_checkpointing
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@@ -1,12 +0,0 @@
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accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \
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--dataset_base_path data/example_image_dataset \
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--dataset_metadata_path data/example_image_dataset/metadata.csv \
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--max_pixels 1048576 \
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--dataset_repeat 400 \
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--model_id_with_origin_paths "black-forest-labs/FLUX.1-Krea-dev:flux1-krea-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \
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--learning_rate 1e-5 \
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--num_epochs 1 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/FLUX.1-Krea-dev_full" \
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--trainable_models "dit" \
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--use_gradient_checkpointing
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@@ -1,14 +0,0 @@
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accelerate launch examples/flux/model_training/train.py \
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--dataset_base_path data/example_image_dataset \
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--dataset_metadata_path data/example_image_dataset/metadata_attrictrl.csv \
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--data_file_keys "image" \
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--max_pixels 1048576 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,DiffSynth-Studio/AttriCtrl-FLUX.1-Dev:models/brightness.safetensors" \
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--learning_rate 1e-5 \
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--num_epochs 1 \
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--remove_prefix_in_ckpt "pipe.value_controller.encoders.0." \
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--output_path "./models/train/FLUX.1-dev-AttriCtrl_full" \
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--trainable_models "value_controller" \
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--extra_inputs "value_controller_inputs" \
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--use_gradient_checkpointing
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@@ -1,14 +0,0 @@
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accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \
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--dataset_base_path data/example_image_dataset \
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--dataset_metadata_path data/example_image_dataset/metadata_controlnet_inpaint.csv \
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--data_file_keys "image,controlnet_image,controlnet_inpaint_mask" \
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--max_pixels 1048576 \
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--dataset_repeat 400 \
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--model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta:diffusion_pytorch_model.safetensors" \
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--learning_rate 1e-5 \
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--num_epochs 1 \
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--remove_prefix_in_ckpt "pipe.controlnet.models.0." \
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--output_path "./models/train/FLUX.1-dev-Controlnet-Inpainting-Beta_full" \
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--trainable_models "controlnet" \
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--extra_inputs "controlnet_image,controlnet_inpaint_mask" \
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--use_gradient_checkpointing
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@@ -1,14 +0,0 @@
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accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \
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--dataset_base_path data/example_image_dataset \
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--dataset_metadata_path data/example_image_dataset/metadata_controlnet_canny.csv \
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--data_file_keys "image,controlnet_image" \
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--max_pixels 1048576 \
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--dataset_repeat 400 \
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--model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,InstantX/FLUX.1-dev-Controlnet-Union-alpha:diffusion_pytorch_model.safetensors" \
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--learning_rate 1e-5 \
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--num_epochs 1 \
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--remove_prefix_in_ckpt "pipe.controlnet.models.0." \
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--output_path "./models/train/FLUX.1-dev-Controlnet-Union-alpha_full" \
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--trainable_models "controlnet" \
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--extra_inputs "controlnet_image,controlnet_processor_id" \
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--use_gradient_checkpointing
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@@ -1,14 +0,0 @@
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accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \
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--dataset_base_path data/example_image_dataset \
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--dataset_metadata_path data/example_image_dataset/metadata_controlnet_upscale.csv \
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--data_file_keys "image,controlnet_image" \
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--max_pixels 1048576 \
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--dataset_repeat 400 \
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--model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,jasperai/Flux.1-dev-Controlnet-Upscaler:diffusion_pytorch_model.safetensors" \
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--learning_rate 1e-5 \
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--num_epochs 1 \
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--remove_prefix_in_ckpt "pipe.controlnet.models.0." \
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--output_path "./models/train/FLUX.1-dev-Controlnet-Upscaler_full" \
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--trainable_models "controlnet" \
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--extra_inputs "controlnet_image" \
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--use_gradient_checkpointing
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@@ -1,14 +0,0 @@
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accelerate launch examples/flux/model_training/train.py \
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--dataset_base_path data/example_image_dataset \
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--dataset_metadata_path data/example_image_dataset/metadata_ipadapter.csv \
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--data_file_keys "image,ipadapter_images" \
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--max_pixels 1048576 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,InstantX/FLUX.1-dev-IP-Adapter:ip-adapter.bin,google/siglip-so400m-patch14-384:" \
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--learning_rate 1e-5 \
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--num_epochs 1 \
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--remove_prefix_in_ckpt "pipe.ipadapter." \
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--output_path "./models/train/FLUX.1-dev-IP-Adapter_full" \
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--trainable_models "ipadapter" \
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--extra_inputs "ipadapter_images" \
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--use_gradient_checkpointing
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@@ -1,14 +0,0 @@
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accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \
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--dataset_base_path data/example_image_dataset \
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--dataset_metadata_path data/example_image_dataset/metadata_infiniteyou.csv \
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--data_file_keys "image,controlnet_image,infinityou_id_image" \
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--max_pixels 1048576 \
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--dataset_repeat 400 \
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--model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,ByteDance/InfiniteYou:infu_flux_v1.0/aes_stage2/image_proj_model.bin,ByteDance/InfiniteYou:infu_flux_v1.0/aes_stage2/InfuseNetModel/*.safetensors" \
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--learning_rate 1e-5 \
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--num_epochs 1 \
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--remove_prefix_in_ckpt "pipe." \
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--output_path "./models/train/FLUX.1-dev-InfiniteYou_full" \
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--trainable_models "controlnet,image_proj_model" \
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--extra_inputs "controlnet_image,infinityou_id_image,infinityou_guidance" \
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--use_gradient_checkpointing
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@@ -1,14 +0,0 @@
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accelerate launch examples/flux/model_training/train.py \
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--dataset_base_path data/example_image_dataset \
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--dataset_metadata_path data/example_image_dataset/metadata_lora_encoder.csv \
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--data_file_keys "image" \
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--max_pixels 1048576 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev:model.safetensors" \
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--learning_rate 1e-5 \
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--num_epochs 1 \
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--remove_prefix_in_ckpt "pipe.lora_encoder." \
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--output_path "./models/train/FLUX.1-dev-LoRA-Encoder_full" \
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--trainable_models "lora_encoder" \
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--extra_inputs "lora_encoder_inputs" \
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--use_gradient_checkpointing
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@@ -1,12 +0,0 @@
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accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \
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--dataset_base_path data/example_image_dataset \
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--dataset_metadata_path data/example_image_dataset/metadata.csv \
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--max_pixels 1048576 \
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--dataset_repeat 400 \
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--model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \
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--learning_rate 1e-5 \
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--num_epochs 1 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/FLUX.1-dev_full" \
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--trainable_models "dit" \
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--use_gradient_checkpointing
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@@ -1,14 +0,0 @@
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accelerate launch --config_file examples/flux/model_training/full/accelerate_config_zero2offload.yaml examples/flux/model_training/train.py \
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--dataset_base_path data/example_image_dataset \
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--dataset_metadata_path data/example_image_dataset/metadata_nexusgen_edit.csv \
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--data_file_keys "image,nexus_gen_reference_image" \
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--max_pixels 262144 \
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--dataset_repeat 400 \
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--model_id_with_origin_paths "DiffSynth-Studio/Nexus-GenV2:model*.safetensors,DiffSynth-Studio/Nexus-GenV2:edit_decoder.bin,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \
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--learning_rate 1e-5 \
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--num_epochs 1 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/FLUX.1-NexusGen-Edit_full" \
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--trainable_models "dit" \
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--extra_inputs "nexus_gen_reference_image" \
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--use_gradient_checkpointing_offload
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@@ -1,14 +0,0 @@
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accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \
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--dataset_base_path data/example_image_dataset \
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--dataset_metadata_path data/example_image_dataset/metadata_step1x.csv \
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--data_file_keys "image,step1x_reference_image" \
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--max_pixels 1048576 \
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--dataset_repeat 400 \
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--model_id_with_origin_paths "Qwen/Qwen2.5-VL-7B-Instruct:,stepfun-ai/Step1X-Edit:step1x-edit-i1258.safetensors,stepfun-ai/Step1X-Edit:vae.safetensors" \
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--learning_rate 1e-5 \
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--num_epochs 1 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/Step1X-Edit_full" \
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--trainable_models "dit" \
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--extra_inputs "step1x_reference_image" \
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--use_gradient_checkpointing_offload
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@@ -1,22 +0,0 @@
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compute_environment: LOCAL_MACHINE
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debug: false
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deepspeed_config:
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gradient_accumulation_steps: 1
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offload_optimizer_device: none
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offload_param_device: none
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zero3_init_flag: false
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zero_stage: 2
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distributed_type: DEEPSPEED
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downcast_bf16: 'no'
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enable_cpu_affinity: false
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machine_rank: 0
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main_training_function: main
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mixed_precision: bf16
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num_machines: 1
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num_processes: 8
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rdzv_backend: static
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same_network: true
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tpu_env: []
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tpu_use_cluster: false
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tpu_use_sudo: false
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use_cpu: false
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@@ -1,22 +0,0 @@
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compute_environment: LOCAL_MACHINE
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debug: false
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deepspeed_config:
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gradient_accumulation_steps: 1
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offload_optimizer_device: 'cpu'
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offload_param_device: 'cpu'
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zero3_init_flag: false
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zero_stage: 2
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distributed_type: DEEPSPEED
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downcast_bf16: 'no'
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enable_cpu_affinity: false
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machine_rank: 0
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main_training_function: main
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mixed_precision: bf16
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num_machines: 1
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num_processes: 8
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rdzv_backend: static
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same_network: true
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tpu_env: []
|
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tpu_use_cluster: false
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||||
tpu_use_sudo: false
|
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use_cpu: false
|
||||
@@ -1,15 +0,0 @@
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accelerate launch examples/flux/model_training/train.py \
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--dataset_base_path data/example_image_dataset \
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--dataset_metadata_path data/example_image_dataset/metadata.csv \
|
||||
--max_pixels 1048576 \
|
||||
--dataset_repeat 50 \
|
||||
--model_id_with_origin_paths "ostris/Flex.2-preview:Flex.2-preview.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \
|
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--learning_rate 1e-4 \
|
||||
--num_epochs 5 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/FLEX.2-preview_lora" \
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--lora_base_model "dit" \
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||||
--lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \
|
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--lora_rank 32 \
|
||||
--align_to_opensource_format \
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--use_gradient_checkpointing
|
||||
@@ -1,17 +0,0 @@
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accelerate launch examples/flux/model_training/train.py \
|
||||
--dataset_base_path data/example_image_dataset \
|
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--dataset_metadata_path data/example_image_dataset/metadata_kontext.csv \
|
||||
--data_file_keys "image,kontext_images" \
|
||||
--max_pixels 1048576 \
|
||||
--dataset_repeat 400 \
|
||||
--model_id_with_origin_paths "black-forest-labs/FLUX.1-Kontext-dev:flux1-kontext-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \
|
||||
--learning_rate 1e-4 \
|
||||
--num_epochs 5 \
|
||||
--remove_prefix_in_ckpt "pipe.dit." \
|
||||
--output_path "./models/train/FLUX.1-Kontext-dev_lora" \
|
||||
--lora_base_model "dit" \
|
||||
--lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \
|
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--lora_rank 32 \
|
||||
--align_to_opensource_format \
|
||||
--extra_inputs "kontext_images" \
|
||||
--use_gradient_checkpointing
|
||||
@@ -1,15 +0,0 @@
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||||
accelerate launch examples/flux/model_training/train.py \
|
||||
--dataset_base_path data/example_image_dataset \
|
||||
--dataset_metadata_path data/example_image_dataset/metadata.csv \
|
||||
--max_pixels 1048576 \
|
||||
--dataset_repeat 50 \
|
||||
--model_id_with_origin_paths "black-forest-labs/FLUX.1-Krea-dev:flux1-krea-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \
|
||||
--learning_rate 1e-4 \
|
||||
--num_epochs 5 \
|
||||
--remove_prefix_in_ckpt "pipe.dit." \
|
||||
--output_path "./models/train/FLUX.1-Krea-dev_lora" \
|
||||
--lora_base_model "dit" \
|
||||
--lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \
|
||||
--lora_rank 32 \
|
||||
--align_to_opensource_format \
|
||||
--use_gradient_checkpointing
|
||||
@@ -1,17 +0,0 @@
|
||||
accelerate launch examples/flux/model_training/train.py \
|
||||
--dataset_base_path data/example_image_dataset \
|
||||
--dataset_metadata_path data/example_image_dataset/metadata_attrictrl.csv \
|
||||
--data_file_keys "image" \
|
||||
--max_pixels 1048576 \
|
||||
--dataset_repeat 100 \
|
||||
--model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,DiffSynth-Studio/AttriCtrl-FLUX.1-Dev:models/brightness.safetensors" \
|
||||
--learning_rate 1e-4 \
|
||||
--num_epochs 5 \
|
||||
--remove_prefix_in_ckpt "pipe.dit." \
|
||||
--output_path "./models/train/FLUX.1-dev-AttriCtrl_lora" \
|
||||
--lora_base_model "dit" \
|
||||
--lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \
|
||||
--lora_rank 32 \
|
||||
--extra_inputs "value_controller_inputs" \
|
||||
--align_to_opensource_format \
|
||||
--use_gradient_checkpointing
|
||||
@@ -1,17 +0,0 @@
|
||||
accelerate launch examples/flux/model_training/train.py \
|
||||
--dataset_base_path data/example_image_dataset \
|
||||
--dataset_metadata_path data/example_image_dataset/metadata_controlnet_inpaint.csv \
|
||||
--data_file_keys "image,controlnet_image,controlnet_inpaint_mask" \
|
||||
--max_pixels 1048576 \
|
||||
--dataset_repeat 100 \
|
||||
--model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta:diffusion_pytorch_model.safetensors" \
|
||||
--learning_rate 1e-4 \
|
||||
--num_epochs 5 \
|
||||
--remove_prefix_in_ckpt "pipe.dit." \
|
||||
--output_path "./models/train/FLUX.1-dev-Controlnet-Inpainting-Beta_lora" \
|
||||
--lora_base_model "dit" \
|
||||
--lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \
|
||||
--lora_rank 32 \
|
||||
--extra_inputs "controlnet_image,controlnet_inpaint_mask" \
|
||||
--align_to_opensource_format \
|
||||
--use_gradient_checkpointing
|
||||
@@ -1,17 +0,0 @@
|
||||
accelerate launch examples/flux/model_training/train.py \
|
||||
--dataset_base_path data/example_image_dataset \
|
||||
--dataset_metadata_path data/example_image_dataset/metadata_controlnet_canny.csv \
|
||||
--data_file_keys "image,controlnet_image" \
|
||||
--max_pixels 1048576 \
|
||||
--dataset_repeat 100 \
|
||||
--model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,InstantX/FLUX.1-dev-Controlnet-Union-alpha:diffusion_pytorch_model.safetensors" \
|
||||
--learning_rate 1e-4 \
|
||||
--num_epochs 5 \
|
||||
--remove_prefix_in_ckpt "pipe.dit." \
|
||||
--output_path "./models/train/FLUX.1-dev-Controlnet-Union-alpha_lora" \
|
||||
--lora_base_model "dit" \
|
||||
--lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \
|
||||
--lora_rank 32 \
|
||||
--extra_inputs "controlnet_image,controlnet_processor_id" \
|
||||
--align_to_opensource_format \
|
||||
--use_gradient_checkpointing
|
||||
@@ -1,17 +0,0 @@
|
||||
accelerate launch examples/flux/model_training/train.py \
|
||||
--dataset_base_path data/example_image_dataset \
|
||||
--dataset_metadata_path data/example_image_dataset/metadata_controlnet_upscale.csv \
|
||||
--data_file_keys "image,controlnet_image" \
|
||||
--max_pixels 1048576 \
|
||||
--dataset_repeat 100 \
|
||||
--model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,jasperai/Flux.1-dev-Controlnet-Upscaler:diffusion_pytorch_model.safetensors" \
|
||||
--learning_rate 1e-4 \
|
||||
--num_epochs 5 \
|
||||
--remove_prefix_in_ckpt "pipe.dit." \
|
||||
--output_path "./models/train/FLUX.1-dev-Controlnet-Upscaler_lora" \
|
||||
--lora_base_model "dit" \
|
||||
--lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \
|
||||
--lora_rank 32 \
|
||||
--extra_inputs "controlnet_image" \
|
||||
--align_to_opensource_format \
|
||||
--use_gradient_checkpointing
|
||||
@@ -1,17 +0,0 @@
|
||||
accelerate launch examples/flux/model_training/train.py \
|
||||
--dataset_base_path data/example_image_dataset \
|
||||
--dataset_metadata_path data/example_image_dataset/metadata_eligen.json \
|
||||
--data_file_keys "image,eligen_entity_masks" \
|
||||
--max_pixels 1048576 \
|
||||
--dataset_repeat 50 \
|
||||
--model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \
|
||||
--learning_rate 1e-4 \
|
||||
--num_epochs 5 \
|
||||
--remove_prefix_in_ckpt "pipe.dit." \
|
||||
--output_path "./models/train/FLUX.1-dev-EliGen_lora" \
|
||||
--lora_base_model "dit" \
|
||||
--lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \
|
||||
--lora_rank 32 \
|
||||
--align_to_opensource_format \
|
||||
--extra_inputs "eligen_entity_masks,eligen_entity_prompts" \
|
||||
--use_gradient_checkpointing
|
||||
@@ -1,17 +0,0 @@
|
||||
accelerate launch examples/flux/model_training/train.py \
|
||||
--dataset_base_path data/example_image_dataset \
|
||||
--dataset_metadata_path data/example_image_dataset/metadata_ipadapter.csv \
|
||||
--data_file_keys "image,ipadapter_images" \
|
||||
--max_pixels 1048576 \
|
||||
--dataset_repeat 50 \
|
||||
--model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,InstantX/FLUX.1-dev-IP-Adapter:ip-adapter.bin,google/siglip-so400m-patch14-384:" \
|
||||
--learning_rate 1e-4 \
|
||||
--num_epochs 5 \
|
||||
--remove_prefix_in_ckpt "pipe.dit." \
|
||||
--output_path "./models/train/FLUX.1-dev-IP-Adapter_lora" \
|
||||
--lora_base_model "dit" \
|
||||
--lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \
|
||||
--lora_rank 32 \
|
||||
--extra_inputs "ipadapter_images" \
|
||||
--align_to_opensource_format \
|
||||
--use_gradient_checkpointing
|
||||
@@ -1,17 +0,0 @@
|
||||
accelerate launch examples/flux/model_training/train.py \
|
||||
--dataset_base_path data/example_image_dataset \
|
||||
--dataset_metadata_path data/example_image_dataset/metadata_infiniteyou.csv \
|
||||
--data_file_keys "image,controlnet_image,infinityou_id_image" \
|
||||
--max_pixels 1048576 \
|
||||
--dataset_repeat 100 \
|
||||
--model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,ByteDance/InfiniteYou:infu_flux_v1.0/aes_stage2/image_proj_model.bin,ByteDance/InfiniteYou:infu_flux_v1.0/aes_stage2/InfuseNetModel/*.safetensors" \
|
||||
--learning_rate 1e-4 \
|
||||
--num_epochs 5 \
|
||||
--remove_prefix_in_ckpt "pipe.dit." \
|
||||
--output_path "./models/train/FLUX.1-dev-InfiniteYou_lora" \
|
||||
--lora_base_model "dit" \
|
||||
--lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \
|
||||
--lora_rank 32 \
|
||||
--extra_inputs "controlnet_image,infinityou_id_image,infinityou_guidance" \
|
||||
--align_to_opensource_format \
|
||||
--use_gradient_checkpointing
|
||||
@@ -1,15 +0,0 @@
|
||||
accelerate launch examples/flux/model_training/train.py \
|
||||
--dataset_base_path data/example_image_dataset \
|
||||
--dataset_metadata_path data/example_image_dataset/metadata.csv \
|
||||
--max_pixels 1048576 \
|
||||
--dataset_repeat 50 \
|
||||
--model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \
|
||||
--learning_rate 1e-4 \
|
||||
--num_epochs 5 \
|
||||
--remove_prefix_in_ckpt "pipe.dit." \
|
||||
--output_path "./models/train/FLUX.1-dev_lora" \
|
||||
--lora_base_model "dit" \
|
||||
--lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \
|
||||
--lora_rank 32 \
|
||||
--align_to_opensource_format \
|
||||
--use_gradient_checkpointing
|
||||
@@ -1,17 +0,0 @@
|
||||
accelerate launch examples/flux/model_training/train.py \
|
||||
--dataset_base_path data/example_image_dataset \
|
||||
--dataset_metadata_path data/example_image_dataset/metadata_nexusgen_edit.csv \
|
||||
--data_file_keys "image,nexus_gen_reference_image" \
|
||||
--max_pixels 1048576 \
|
||||
--dataset_repeat 400 \
|
||||
--model_id_with_origin_paths "DiffSynth-Studio/Nexus-GenV2:model*.safetensors,DiffSynth-Studio/Nexus-GenV2:edit_decoder.bin,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \
|
||||
--learning_rate 1e-4 \
|
||||
--num_epochs 5 \
|
||||
--remove_prefix_in_ckpt "pipe.dit." \
|
||||
--output_path "./models/train/FLUX.1-NexusGen-Edit_lora" \
|
||||
--lora_base_model "dit" \
|
||||
--lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \
|
||||
--lora_rank 32 \
|
||||
--align_to_opensource_format \
|
||||
--extra_inputs "nexus_gen_reference_image" \
|
||||
--use_gradient_checkpointing
|
||||
@@ -1,17 +0,0 @@
|
||||
accelerate launch examples/flux/model_training/train.py \
|
||||
--dataset_base_path data/example_image_dataset \
|
||||
--dataset_metadata_path data/example_image_dataset/metadata_step1x.csv \
|
||||
--data_file_keys "image,step1x_reference_image" \
|
||||
--max_pixels 1048576 \
|
||||
--dataset_repeat 50 \
|
||||
--model_id_with_origin_paths "Qwen/Qwen2.5-VL-7B-Instruct:,stepfun-ai/Step1X-Edit:step1x-edit-i1258.safetensors,stepfun-ai/Step1X-Edit:vae.safetensors" \
|
||||
--learning_rate 1e-4 \
|
||||
--num_epochs 5 \
|
||||
--remove_prefix_in_ckpt "pipe.dit." \
|
||||
--output_path "./models/train/Step1X-Edit_lora" \
|
||||
--lora_base_model "dit" \
|
||||
--lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \
|
||||
--lora_rank 32 \
|
||||
--extra_inputs "step1x_reference_image" \
|
||||
--align_to_opensource_format \
|
||||
--use_gradient_checkpointing
|
||||
@@ -1,120 +0,0 @@
|
||||
import torch, os, json
|
||||
from diffsynth import load_state_dict
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
|
||||
from diffsynth.trainers.utils import DiffusionTrainingModule, ModelLogger, launch_training_task, flux_parser
|
||||
from diffsynth.models.lora import FluxLoRAConverter
|
||||
from diffsynth.trainers.unified_dataset import UnifiedDataset
|
||||
os.environ["TOKENIZERS_PARALLELISM"] = "false"
|
||||
|
||||
|
||||
|
||||
class FluxTrainingModule(DiffusionTrainingModule):
|
||||
def __init__(
|
||||
self,
|
||||
model_paths=None, model_id_with_origin_paths=None,
|
||||
trainable_models=None,
|
||||
lora_base_model=None, lora_target_modules="a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp", lora_rank=32, lora_checkpoint=None,
|
||||
use_gradient_checkpointing=True,
|
||||
use_gradient_checkpointing_offload=False,
|
||||
extra_inputs=None,
|
||||
):
|
||||
super().__init__()
|
||||
# Load models
|
||||
model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, enable_fp8_training=False)
|
||||
self.pipe = FluxImagePipeline.from_pretrained(torch_dtype=torch.bfloat16, device="cpu", model_configs=model_configs)
|
||||
|
||||
# Training mode
|
||||
self.switch_pipe_to_training_mode(
|
||||
self.pipe, trainable_models,
|
||||
lora_base_model, lora_target_modules, lora_rank, lora_checkpoint=lora_checkpoint,
|
||||
enable_fp8_training=False,
|
||||
)
|
||||
|
||||
# Store other configs
|
||||
self.use_gradient_checkpointing = use_gradient_checkpointing
|
||||
self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload
|
||||
self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else []
|
||||
|
||||
|
||||
def forward_preprocess(self, data):
|
||||
# CFG-sensitive parameters
|
||||
inputs_posi = {"prompt": data["prompt"]}
|
||||
inputs_nega = {"negative_prompt": ""}
|
||||
|
||||
# CFG-unsensitive parameters
|
||||
inputs_shared = {
|
||||
# Assume you are using this pipeline for inference,
|
||||
# please fill in the input parameters.
|
||||
"input_image": data["image"],
|
||||
"height": data["image"].size[1],
|
||||
"width": data["image"].size[0],
|
||||
# Please do not modify the following parameters
|
||||
# unless you clearly know what this will cause.
|
||||
"cfg_scale": 1,
|
||||
"embedded_guidance": 1,
|
||||
"t5_sequence_length": 512,
|
||||
"tiled": False,
|
||||
"rand_device": self.pipe.device,
|
||||
"use_gradient_checkpointing": self.use_gradient_checkpointing,
|
||||
"use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload,
|
||||
}
|
||||
|
||||
# Extra inputs
|
||||
controlnet_input = {}
|
||||
for extra_input in self.extra_inputs:
|
||||
if extra_input.startswith("controlnet_"):
|
||||
controlnet_input[extra_input.replace("controlnet_", "")] = data[extra_input]
|
||||
else:
|
||||
inputs_shared[extra_input] = data[extra_input]
|
||||
if len(controlnet_input) > 0:
|
||||
inputs_shared["controlnet_inputs"] = [ControlNetInput(**controlnet_input)]
|
||||
|
||||
# Pipeline units will automatically process the input parameters.
|
||||
for unit in self.pipe.units:
|
||||
inputs_shared, inputs_posi, inputs_nega = self.pipe.unit_runner(unit, self.pipe, inputs_shared, inputs_posi, inputs_nega)
|
||||
return {**inputs_shared, **inputs_posi}
|
||||
|
||||
|
||||
def forward(self, data, inputs=None):
|
||||
if inputs is None: inputs = self.forward_preprocess(data)
|
||||
models = {name: getattr(self.pipe, name) for name in self.pipe.in_iteration_models}
|
||||
loss = self.pipe.training_loss(**models, **inputs)
|
||||
return loss
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = flux_parser()
|
||||
args = parser.parse_args()
|
||||
dataset = UnifiedDataset(
|
||||
base_path=args.dataset_base_path,
|
||||
metadata_path=args.dataset_metadata_path,
|
||||
repeat=args.dataset_repeat,
|
||||
data_file_keys=args.data_file_keys.split(","),
|
||||
main_data_operator=UnifiedDataset.default_image_operator(
|
||||
base_path=args.dataset_base_path,
|
||||
max_pixels=args.max_pixels,
|
||||
height=args.height,
|
||||
width=args.width,
|
||||
height_division_factor=16,
|
||||
width_division_factor=16,
|
||||
)
|
||||
)
|
||||
model = FluxTrainingModule(
|
||||
model_paths=args.model_paths,
|
||||
model_id_with_origin_paths=args.model_id_with_origin_paths,
|
||||
trainable_models=args.trainable_models,
|
||||
lora_base_model=args.lora_base_model,
|
||||
lora_target_modules=args.lora_target_modules,
|
||||
lora_rank=args.lora_rank,
|
||||
lora_checkpoint=args.lora_checkpoint,
|
||||
use_gradient_checkpointing=args.use_gradient_checkpointing,
|
||||
use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload,
|
||||
extra_inputs=args.extra_inputs,
|
||||
)
|
||||
model_logger = ModelLogger(
|
||||
args.output_path,
|
||||
remove_prefix_in_ckpt=args.remove_prefix_in_ckpt,
|
||||
state_dict_converter=FluxLoRAConverter.align_to_opensource_format if args.align_to_opensource_format else lambda x:x,
|
||||
)
|
||||
launch_training_task(dataset, model, model_logger, args=args)
|
||||
@@ -1,20 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
||||
from diffsynth import load_state_dict
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="ostris/Flex.2-preview", origin_file_pattern="Flex.2-preview.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
],
|
||||
)
|
||||
state_dict = load_state_dict("models/train/FLEX.2-preview_full/epoch-0.safetensors")
|
||||
pipe.dit.load_state_dict(state_dict)
|
||||
|
||||
image = pipe(prompt="dog,white and brown dog, sitting on wall, under pink flowers", seed=0)
|
||||
image.save("image_FLEX.2-preview_full.jpg")
|
||||
@@ -1,26 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
||||
from diffsynth import load_state_dict
|
||||
from PIL import Image
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="flux1-kontext-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
],
|
||||
)
|
||||
state_dict = load_state_dict("models/train/FLUX.1-Kontext-dev_full/epoch-0.safetensors")
|
||||
pipe.dit.load_state_dict(state_dict)
|
||||
|
||||
image = pipe(
|
||||
prompt="Make the dog turn its head around.",
|
||||
kontext_images=Image.open("data/example_image_dataset/2.jpg").resize((768, 768)),
|
||||
height=768, width=768,
|
||||
seed=0
|
||||
)
|
||||
image.save("image_FLUX.1-Kontext-dev_full.jpg")
|
||||
@@ -1,20 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
||||
from diffsynth import load_state_dict
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-Krea-dev", origin_file_pattern="flux1-krea-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
],
|
||||
)
|
||||
state_dict = load_state_dict("models/train/FLUX.1-Krea-dev_full/epoch-0.safetensors")
|
||||
pipe.dit.load_state_dict(state_dict)
|
||||
|
||||
image = pipe(prompt="a dog", seed=0)
|
||||
image.save("image_FLUX.1-Krea-dev_full.jpg")
|
||||
@@ -1,21 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
||||
from diffsynth import load_state_dict
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
ModelConfig(model_id="DiffSynth-Studio/AttriCtrl-FLUX.1-Dev", origin_file_pattern="models/brightness.safetensors")
|
||||
],
|
||||
)
|
||||
state_dict = load_state_dict("models/train/FLUX.1-dev-AttriCtrl_full/epoch-0.safetensors")
|
||||
pipe.value_controller.encoders[0].load_state_dict(state_dict)
|
||||
|
||||
image = pipe(prompt="a cat", seed=0, value_controller_inputs=0.1, rand_device="cuda")
|
||||
image.save("image_FLUX.1-dev-AttriCtrl_full.jpg")
|
||||
@@ -1,31 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
|
||||
from diffsynth import load_state_dict
|
||||
from PIL import Image
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
ModelConfig(model_id="alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", origin_file_pattern="diffusion_pytorch_model.safetensors"),
|
||||
],
|
||||
)
|
||||
state_dict = load_state_dict("models/train/FLUX.1-dev-Controlnet-Inpainting-Beta_full/epoch-0.safetensors")
|
||||
pipe.controlnet.models[0].load_state_dict(state_dict)
|
||||
|
||||
image = pipe(
|
||||
prompt="a cat sitting on a chair, wearing sunglasses",
|
||||
controlnet_inputs=[ControlNetInput(
|
||||
image=Image.open("data/example_image_dataset/inpaint/image_1.jpg"),
|
||||
inpaint_mask=Image.open("data/example_image_dataset/inpaint/mask.jpg"),
|
||||
scale=0.9
|
||||
)],
|
||||
height=1024, width=1024,
|
||||
seed=0, rand_device="cuda",
|
||||
)
|
||||
image.save("image_FLUX.1-dev-Controlnet-Inpainting-Beta_full.jpg")
|
||||
@@ -1,31 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
|
||||
from diffsynth import load_state_dict
|
||||
from PIL import Image
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
ModelConfig(model_id="InstantX/FLUX.1-dev-Controlnet-Union-alpha", origin_file_pattern="diffusion_pytorch_model.safetensors"),
|
||||
],
|
||||
)
|
||||
state_dict = load_state_dict("models/train/FLUX.1-dev-Controlnet-Union-alpha_full/epoch-0.safetensors")
|
||||
pipe.controlnet.models[0].load_state_dict(state_dict)
|
||||
|
||||
image = pipe(
|
||||
prompt="a dog",
|
||||
controlnet_inputs=[ControlNetInput(
|
||||
image=Image.open("data/example_image_dataset/canny/image_1.jpg"),
|
||||
scale=0.9,
|
||||
processor_id="canny",
|
||||
)],
|
||||
height=768, width=768,
|
||||
seed=0, rand_device="cuda",
|
||||
)
|
||||
image.save("image_FLUX.1-dev-Controlnet-Union-alpha_full.jpg")
|
||||
@@ -1,30 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
|
||||
from diffsynth import load_state_dict
|
||||
from PIL import Image
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
ModelConfig(model_id="jasperai/Flux.1-dev-Controlnet-Upscaler", origin_file_pattern="diffusion_pytorch_model.safetensors"),
|
||||
],
|
||||
)
|
||||
state_dict = load_state_dict("models/train/FLUX.1-dev-Controlnet-Upscaler_full/epoch-0.safetensors")
|
||||
pipe.controlnet.models[0].load_state_dict(state_dict)
|
||||
|
||||
image = pipe(
|
||||
prompt="a dog",
|
||||
controlnet_inputs=[ControlNetInput(
|
||||
image=Image.open("data/example_image_dataset/upscale/image_1.jpg"),
|
||||
scale=0.9
|
||||
)],
|
||||
height=768, width=768,
|
||||
seed=0, rand_device="cuda",
|
||||
)
|
||||
image.save("image_FLUX.1-dev-Controlnet-Upscaler_full.jpg")
|
||||
@@ -1,28 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
||||
from diffsynth import load_state_dict
|
||||
from PIL import Image
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
ModelConfig(model_id="InstantX/FLUX.1-dev-IP-Adapter", origin_file_pattern="ip-adapter.bin"),
|
||||
ModelConfig(model_id="google/siglip-so400m-patch14-384"),
|
||||
],
|
||||
)
|
||||
state_dict = load_state_dict("models/train/FLUX.1-dev-IP-Adapter_full/epoch-0.safetensors")
|
||||
pipe.ipadapter.load_state_dict(state_dict)
|
||||
|
||||
image = pipe(
|
||||
prompt="a dog",
|
||||
ipadapter_images=Image.open("data/example_image_dataset/1.jpg"),
|
||||
height=768, width=768,
|
||||
seed=0
|
||||
)
|
||||
image.save("image_FLUX.1-dev-IP-Adapter_full.jpg")
|
||||
@@ -1,33 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
|
||||
from diffsynth import load_state_dict
|
||||
from PIL import Image
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
ModelConfig(model_id="ByteDance/InfiniteYou", origin_file_pattern="infu_flux_v1.0/aes_stage2/image_proj_model.bin"),
|
||||
ModelConfig(model_id="ByteDance/InfiniteYou", origin_file_pattern="infu_flux_v1.0/aes_stage2/InfuseNetModel/*.safetensors"),
|
||||
],
|
||||
)
|
||||
state_dict = load_state_dict("models/train/FLUX.1-dev-InfiniteYou_full/epoch-0.safetensors")
|
||||
state_dict_projector = {i.replace("image_proj_model.", ""): state_dict[i] for i in state_dict if i.startswith("image_proj_model.")}
|
||||
pipe.image_proj_model.load_state_dict(state_dict_projector)
|
||||
state_dict_controlnet = {i.replace("controlnet.models.0.", ""): state_dict[i] for i in state_dict if i.startswith("controlnet.models.0.")}
|
||||
pipe.controlnet.models[0].load_state_dict(state_dict_controlnet)
|
||||
|
||||
image = pipe(
|
||||
prompt="a man with a red hat",
|
||||
controlnet_inputs=[ControlNetInput(
|
||||
image=Image.open("data/example_image_dataset/infiniteyou/image_1.jpg"),
|
||||
)],
|
||||
height=1024, width=1024,
|
||||
seed=0, rand_device="cuda",
|
||||
)
|
||||
image.save("image_FLUX.1-dev-InfiniteYou_full.jpg")
|
||||
@@ -1,25 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
||||
from diffsynth import load_state_dict
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
ModelConfig(model_id="DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev", origin_file_pattern="model.safetensors"),
|
||||
],
|
||||
)
|
||||
pipe.enable_lora_magic()
|
||||
state_dict = load_state_dict("models/train/FLUX.1-dev-LoRA-Encoder_full/epoch-0.safetensors")
|
||||
pipe.lora_encoder.load_state_dict(state_dict)
|
||||
|
||||
lora = ModelConfig(model_id="VoidOc/flux_animal_forest1", origin_file_pattern="20.safetensors")
|
||||
pipe.load_lora(pipe.dit, lora, hotload=True) # Use `pipe.clear_lora()` to drop the loaded LoRA.
|
||||
|
||||
image = pipe(prompt="", seed=0, lora_encoder_inputs=lora)
|
||||
image.save("image_FLUX.1-dev-LoRA-Encoder_full.jpg")
|
||||
@@ -1,20 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
||||
from diffsynth import load_state_dict
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
],
|
||||
)
|
||||
state_dict = load_state_dict("models/train/FLUX.1-dev_full/epoch-0.safetensors")
|
||||
pipe.dit.load_state_dict(state_dict)
|
||||
|
||||
image = pipe(prompt="a dog", seed=0)
|
||||
image.save("image_FLUX.1-dev_full.jpg")
|
||||
@@ -1,28 +0,0 @@
|
||||
import torch
|
||||
from PIL import Image
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
||||
from diffsynth import load_state_dict
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="model*.safetensors"),
|
||||
ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="edit_decoder.bin"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
],
|
||||
)
|
||||
state_dict = load_state_dict("models/train/FLUX.1-NexusGen-Edit_full/epoch-0.safetensors")
|
||||
pipe.dit.load_state_dict(state_dict)
|
||||
|
||||
ref_image = Image.open("data/example_image_dataset/nexus_gen/image_1.png").convert("RGB")
|
||||
prompt = "Add a pair of sunglasses."
|
||||
image = pipe(
|
||||
prompt=prompt, negative_prompt="",
|
||||
seed=42, cfg_scale=2.0, num_inference_steps=50,
|
||||
nexus_gen_reference_image=ref_image,
|
||||
height=512, width=512,
|
||||
)
|
||||
image.save("NexusGen-Edit_full.jpg")
|
||||
@@ -1,25 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
||||
from diffsynth import load_state_dict
|
||||
from PIL import Image
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="Qwen/Qwen2.5-VL-7B-Instruct"),
|
||||
ModelConfig(model_id="stepfun-ai/Step1X-Edit", origin_file_pattern="step1x-edit-i1258.safetensors"),
|
||||
ModelConfig(model_id="stepfun-ai/Step1X-Edit", origin_file_pattern="vae.safetensors"),
|
||||
],
|
||||
)
|
||||
state_dict = load_state_dict("models/train/Step1X-Edit_full/epoch-0.safetensors")
|
||||
pipe.dit.load_state_dict(state_dict)
|
||||
|
||||
image = pipe(
|
||||
prompt="Make the dog turn its head around.",
|
||||
step1x_reference_image=Image.open("data/example_image_dataset/2.jpg").resize((768, 768)),
|
||||
height=768, width=768, cfg_scale=6,
|
||||
seed=0
|
||||
)
|
||||
image.save("image_Step1X-Edit_full.jpg")
|
||||
@@ -1,18 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="ostris/Flex.2-preview", origin_file_pattern="Flex.2-preview.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
],
|
||||
)
|
||||
pipe.load_lora(pipe.dit, "models/train/FLEX.2-preview_lora/epoch-4.safetensors", alpha=1)
|
||||
|
||||
image = pipe(prompt="dog,white and brown dog, sitting on wall, under pink flowers", seed=0)
|
||||
image.save("image_FLEX.2-preview_lora.jpg")
|
||||
@@ -1,24 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
||||
from PIL import Image
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="flux1-kontext-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
],
|
||||
)
|
||||
pipe.load_lora(pipe.dit, "models/train/FLUX.1-Kontext-dev_lora/epoch-4.safetensors", alpha=1)
|
||||
|
||||
image = pipe(
|
||||
prompt="Make the dog turn its head around.",
|
||||
kontext_images=Image.open("data/example_image_dataset/2.jpg").resize((768, 768)),
|
||||
height=768, width=768,
|
||||
seed=0
|
||||
)
|
||||
image.save("image_FLUX.1-Kontext-dev_lora.jpg")
|
||||
@@ -1,18 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-Krea-dev", origin_file_pattern="flux1-krea-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
],
|
||||
)
|
||||
pipe.load_lora(pipe.dit, "models/train/FLUX.1-Krea-dev_lora/epoch-4.safetensors", alpha=1)
|
||||
|
||||
image = pipe(prompt="a dog", seed=0)
|
||||
image.save("image_FLUX.1-Krea-dev_lora.jpg")
|
||||
@@ -1,19 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
ModelConfig(model_id="DiffSynth-Studio/AttriCtrl-FLUX.1-Dev", origin_file_pattern="models/brightness.safetensors")
|
||||
],
|
||||
)
|
||||
pipe.load_lora(pipe.dit, "models/train/FLUX.1-dev-AttriCtrl_lora/epoch-3.safetensors", alpha=1)
|
||||
|
||||
image = pipe(prompt="a cat", seed=0, value_controller_inputs=0.1, rand_device="cuda")
|
||||
image.save("image_FLUX.1-dev-AttriCtrl_lora.jpg")
|
||||
@@ -1,29 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
|
||||
from PIL import Image
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
ModelConfig(model_id="alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", origin_file_pattern="diffusion_pytorch_model.safetensors"),
|
||||
],
|
||||
)
|
||||
pipe.load_lora(pipe.dit, "models/train/FLUX.1-dev-Controlnet-Inpainting-Beta_lora/epoch-4.safetensors", alpha=1)
|
||||
|
||||
image = pipe(
|
||||
prompt="a cat sitting on a chair, wearing sunglasses",
|
||||
controlnet_inputs=[ControlNetInput(
|
||||
image=Image.open("data/example_image_dataset/inpaint/image_1.jpg"),
|
||||
inpaint_mask=Image.open("data/example_image_dataset/inpaint/mask.jpg"),
|
||||
scale=0.9
|
||||
)],
|
||||
height=1024, width=1024,
|
||||
seed=0, rand_device="cuda",
|
||||
)
|
||||
image.save("image_FLUX.1-dev-Controlnet-Inpainting-Beta_lora.jpg")
|
||||
@@ -1,29 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
|
||||
from PIL import Image
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
ModelConfig(model_id="InstantX/FLUX.1-dev-Controlnet-Union-alpha", origin_file_pattern="diffusion_pytorch_model.safetensors"),
|
||||
],
|
||||
)
|
||||
pipe.load_lora(pipe.dit, "models/train/FLUX.1-dev-Controlnet-Union-alpha_lora/epoch-4.safetensors", alpha=1)
|
||||
|
||||
image = pipe(
|
||||
prompt="a dog",
|
||||
controlnet_inputs=[ControlNetInput(
|
||||
image=Image.open("data/example_image_dataset/canny/image_1.jpg"),
|
||||
scale=0.9,
|
||||
processor_id="canny",
|
||||
)],
|
||||
height=768, width=768,
|
||||
seed=0, rand_device="cuda",
|
||||
)
|
||||
image.save("image_FLUX.1-dev-Controlnet-Union-alpha_lora.jpg")
|
||||
@@ -1,28 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
|
||||
from PIL import Image
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
ModelConfig(model_id="jasperai/Flux.1-dev-Controlnet-Upscaler", origin_file_pattern="diffusion_pytorch_model.safetensors"),
|
||||
],
|
||||
)
|
||||
pipe.load_lora(pipe.dit, "models/train/FLUX.1-dev-Controlnet-Upscaler_lora/epoch-4.safetensors", alpha=1)
|
||||
|
||||
image = pipe(
|
||||
prompt="a dog",
|
||||
controlnet_inputs=[ControlNetInput(
|
||||
image=Image.open("data/example_image_dataset/upscale/image_1.jpg"),
|
||||
scale=0.9
|
||||
)],
|
||||
height=768, width=768,
|
||||
seed=0, rand_device="cuda",
|
||||
)
|
||||
image.save("image_FLUX.1-dev-Controlnet-Upscaler_lora.jpg")
|
||||
@@ -1,33 +0,0 @@
|
||||
import torch
|
||||
from PIL import Image
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
],
|
||||
)
|
||||
|
||||
pipe.load_lora(pipe.dit, "models/train/FLUX.1-dev-EliGen_lora/epoch-4.safetensors", alpha=1)
|
||||
|
||||
entity_prompts = ["A beautiful girl", "sign 'Entity Control'", "shorts", "shirt"]
|
||||
global_prompt = "A beautiful girl wearing shirt and shorts in the street, holding a sign 'Entity Control'"
|
||||
masks = [Image.open(f"data/example_image_dataset/eligen/{i}.png").convert('RGB') for i in range(len(entity_prompts))]
|
||||
# generate image
|
||||
image = pipe(
|
||||
prompt=global_prompt,
|
||||
cfg_scale=1.0,
|
||||
num_inference_steps=50,
|
||||
embedded_guidance=3.5,
|
||||
seed=42,
|
||||
height=1024,
|
||||
width=1024,
|
||||
eligen_entity_prompts=entity_prompts,
|
||||
eligen_entity_masks=masks,
|
||||
)
|
||||
image.save(f"EliGen_lora.png")
|
||||
@@ -1,26 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
||||
from PIL import Image
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
ModelConfig(model_id="InstantX/FLUX.1-dev-IP-Adapter", origin_file_pattern="ip-adapter.bin"),
|
||||
ModelConfig(model_id="google/siglip-so400m-patch14-384"),
|
||||
],
|
||||
)
|
||||
pipe.load_lora(pipe.dit, "models/train/FLUX.1-dev-IP-Adapter_lora/epoch-4.safetensors", alpha=1)
|
||||
|
||||
image = pipe(
|
||||
prompt="dog,white and brown dog, sitting on wall, under pink flowers",
|
||||
ipadapter_images=Image.open("data/example_image_dataset/1.jpg"),
|
||||
height=768, width=768,
|
||||
seed=0
|
||||
)
|
||||
image.save("image_FLUX.1-dev-IP-Adapter_lora.jpg")
|
||||
@@ -1,28 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
|
||||
from PIL import Image
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
ModelConfig(model_id="ByteDance/InfiniteYou", origin_file_pattern="infu_flux_v1.0/aes_stage2/image_proj_model.bin"),
|
||||
ModelConfig(model_id="ByteDance/InfiniteYou", origin_file_pattern="infu_flux_v1.0/aes_stage2/InfuseNetModel/*.safetensors"),
|
||||
],
|
||||
)
|
||||
pipe.load_lora(pipe.dit, "models/train/FLUX.1-dev-InfiniteYou_lora/epoch-4.safetensors", alpha=1)
|
||||
|
||||
image = pipe(
|
||||
prompt="a man with a red hat",
|
||||
controlnet_inputs=[ControlNetInput(
|
||||
image=Image.open("data/example_image_dataset/infiniteyou/image_1.jpg"),
|
||||
)],
|
||||
height=1024, width=1024,
|
||||
seed=0, rand_device="cuda",
|
||||
)
|
||||
image.save("image_FLUX.1-dev-InfiniteYou_lora.jpg")
|
||||
@@ -1,18 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
],
|
||||
)
|
||||
pipe.load_lora(pipe.dit, "models/train/FLUX.1-dev_lora/epoch-4.safetensors", alpha=1)
|
||||
|
||||
image = pipe(prompt="a dog", seed=0)
|
||||
image.save("image_FLUX.1-dev_lora.jpg")
|
||||
@@ -1,26 +0,0 @@
|
||||
import torch
|
||||
from PIL import Image
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="model*.safetensors"),
|
||||
ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="edit_decoder.bin"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
],
|
||||
)
|
||||
pipe.load_lora(pipe.dit, "models/train/FLUX.1-NexusGen-Edit_lora/epoch-4.safetensors", alpha=1)
|
||||
|
||||
ref_image = Image.open("data/example_image_dataset/nexus_gen/image_1.png").convert("RGB")
|
||||
prompt = "Add a pair of sunglasses."
|
||||
image = pipe(
|
||||
prompt=prompt, negative_prompt="",
|
||||
seed=42, cfg_scale=1.0, num_inference_steps=50,
|
||||
nexus_gen_reference_image=ref_image,
|
||||
height=512, width=512,
|
||||
)
|
||||
image.save("NexusGen-Edit_lora.jpg")
|
||||
@@ -1,23 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
||||
from PIL import Image
|
||||
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="Qwen/Qwen2.5-VL-7B-Instruct"),
|
||||
ModelConfig(model_id="stepfun-ai/Step1X-Edit", origin_file_pattern="step1x-edit-i1258.safetensors"),
|
||||
ModelConfig(model_id="stepfun-ai/Step1X-Edit", origin_file_pattern="vae.safetensors"),
|
||||
],
|
||||
)
|
||||
pipe.load_lora(pipe.dit, "models/train/Step1X-Edit_lora/epoch-4.safetensors", alpha=1)
|
||||
|
||||
image = pipe(
|
||||
prompt="Make the dog turn its head around.",
|
||||
step1x_reference_image=Image.open("data/example_image_dataset/2.jpg").resize((768, 768)),
|
||||
height=768, width=768, cfg_scale=6,
|
||||
seed=0
|
||||
)
|
||||
image.save("image_Step1X-Edit_lora.jpg")
|
||||
Reference in New Issue
Block a user