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FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/checkpoint-1500/lycoris_config.json

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FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/checkpoint-1500/simpletuner_config.json

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+    "post_upload_script": null,
+    "push_checkpoints_to_hub": false,
+    "push_to_hub_background": false,
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+    "model_card_safe_for_work": false,
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+    "checkpointing_rolling_steps": 0,
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+    "tracker_run_name": "dlay-persona-prodigy-bf16-8xB300-V2-dynamo",
+    "tracker_project_name": "FLUX.2-DEV-DLAY-Persona",
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+        "timeout": "1:30:00"
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+    "weight_dtype": "torch.bfloat16",
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+    "checkpointing_steps": 500,
+    "use_fsdp": false,
+    "model_type_label": "Flux.2",
+    "use_deepspeed_optimizer": false,
+    "use_deepspeed_scheduler": false,
+    "base_weight_dtype": "torch.bfloat16",
+    "is_quanto": false,
+    "is_torchao": false,
+    "is_bnb": false,
+    "pipeline_quantization": false,
+    "pipeline_quantization_base": false,
+    "flow_matching": true,
+    "enable_adamw_bf16": true,
+    "overrode_max_train_steps": false,
+    "total_num_batches": 2959,
+    "num_update_steps_per_epoch": 2959,
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+    "is_schedulefree": true,
+    "is_lr_scheduler_disabled": false,
+    "total_steps_remaining_at_start": 11500
+}

FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/checkpoint-1500/training_state-dlay-subject-data.json

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FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/checkpoint-3000/lycoris_config.json

@@ -0,0 +1,16 @@
+{
+  "algo": "lokr",
+  "multiplier": 1.0,
+  "factor": 12,
+  "linear_dim": 100000,
+  "linear_alpha": 1,
+  "conv_dim": 0,
+  "conv_alpha": 0,
+  "dora_wd": true,
+  "apply_preset": {
+    "target_module": [
+      "Flux2TransformerBlock", 
+      "Flux2SingleTransformerBlock"
+    ]
+  }
+}

FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/checkpoint-3000/training_state.json

@@ -0,0 +1 @@
+{"global_step": 3000, "epoch_step": 3001, "epoch": 2, "exhausted_backends": [], "repeats": {"dlay-subject-data": 0}}

FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/checkpoint-3500/README.md

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+---
+license: other
+base_model: "black-forest-labs/FLUX.2-dev"
+tags:
+  - flux2
+  - flux2-diffusers
+  - text-to-image
+  - image-to-image
+  - diffusers
+  - simpletuner
+  - not-for-all-audiences
+  - lora
+
+  - template:sd-lora
+  - lycoris
+pipeline_tag: text-to-image
+inference: true
+
+---
+
+# FLUX.2-DEV-DLAY-LyCORIS
+
+This is a LyCORIS adapter derived from [black-forest-labs/FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev).
+
+No validation prompt was used during training.
+
+None
+
+
+
+## Validation settings
+- CFG: `4.0`
+- CFG Rescale: `0.0`
+- Steps: `28`
+- Sampler: `FlowMatchEulerDiscreteScheduler`
+- Seed: `42`
+- Resolution: `1024x1024`
+
+
+Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
+
+
+
+
+<Gallery />
+
+The text encoder **was not** trained.
+You may reuse the base model text encoder for inference.
+
+
+## Training settings
+
+- Training epochs: 1
+- Training steps: 3500
+- Learning rate: 1.0
+  - Learning rate schedule: constant
+  - Warmup steps: 0
+- Max grad norm: 1.0
+- Effective batch size: 8
+  - Micro-batch size: 1
+  - Gradient accumulation steps: 1
+  - Number of GPUs: 8
+- Gradient checkpointing: False
+- Prediction type: flow_matching[]
+- Optimizer: prodigy (config=d_coef=2.0,weight_decay=0.01,use_bias_correction=False,schedulefree_c=75,use_orthograd=True)
+- Trainable parameter precision: Pure BF16
+- Base model precision: `no_change`
+- Caption dropout probability: 0.1%
+
+
+
+
+### LyCORIS Config:
+```json
+{
+    "algo": "lokr",
+    "multiplier": 1.0,
+    "factor": 12,
+    "linear_dim": 100000,
+    "linear_alpha": 1,
+    "conv_dim": 0,
+    "conv_alpha": 0,
+    "dora_wd": true,
+    "apply_preset": {
+        "target_module": [
+            "Flux2TransformerBlock",
+            "Flux2SingleTransformerBlock"
+        ]
+    }
+}
+```
+
+
+## Datasets
+
+### dlay-subject-data
+- Repeats: 10
+- Total number of images: ~2152
+- Total number of aspect buckets: 1
+- Resolution: 1.048576 megapixels
+- Cropped: False
+- Crop style: None
+- Crop aspect: None
+- Used for regularisation data: No
+
+
+## Inference
+
+
+```python
+import torch
+from diffusers import DiffusionPipeline
+from lycoris import create_lycoris_from_weights
+
+
+def download_adapter(repo_id: str):
+    import os
+    from huggingface_hub import hf_hub_download
+    adapter_filename = "pytorch_lora_weights.safetensors"
+    cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models'))
+    cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_")
+    path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path)
+    path_to_adapter_file = os.path.join(path_to_adapter, adapter_filename)
+    os.makedirs(path_to_adapter, exist_ok=True)
+    hf_hub_download(
+        repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter
+    )
+
+    return path_to_adapter_file
+    
+model_id = 'black-forest-labs/FLUX.2-dev'
+adapter_repo_id = 'ThatDustyGuy/FLUX.2-DEV-DLAY-LyCORIS'
+adapter_filename = 'pytorch_lora_weights.safetensors'
+adapter_file_path = download_adapter(repo_id=adapter_repo_id)
+pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
+lora_scale = 1.0
+wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer)
+wrapper.merge_to()
+
+prompt = "An astronaut is riding a horse through the jungles of Thailand."
+negative_prompt = 'blurry, cropped, ugly'
+
+## Optional: quantise the model to save on vram.
+## Note: The model was not quantised during training, so it is not necessary to quantise it during inference time.
+#from optimum.quanto import quantize, freeze, qint8
+#quantize(pipeline.transformer, weights=qint8)
+#freeze(pipeline.transformer)
+    
+pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
+model_output = pipeline(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    num_inference_steps=28,
+    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
+    width=1024,
+    height=1024,
+    guidance_scale=4.0,
+).images[0]
+
+model_output.save("output.png", format="PNG")
+
+```
+
+
+
+## Exponential Moving Average (EMA)
+
+SimpleTuner generates a safetensors variant of the EMA weights and a pt file.
+
+The safetensors file is intended to be used for inference, and the pt file is for continuing finetuning.
+
+The EMA model may provide a more well-rounded result, but typically will feel undertrained compared to the full model as it is a running decayed average of the model weights.
+

FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/checkpoint-3500/training_state-dlay-subject-data.json

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FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/checkpoint-4000/README.md

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+---
+license: other
+base_model: "black-forest-labs/FLUX.2-dev"
+tags:
+  - flux2
+  - flux2-diffusers
+  - text-to-image
+  - image-to-image
+  - diffusers
+  - simpletuner
+  - not-for-all-audiences
+  - lora
+
+  - template:sd-lora
+  - lycoris
+pipeline_tag: text-to-image
+inference: true
+
+---
+
+# FLUX.2-DEV-DLAY-LyCORIS
+
+This is a LyCORIS adapter derived from [black-forest-labs/FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev).
+
+No validation prompt was used during training.
+
+None
+
+
+
+## Validation settings
+- CFG: `4.0`
+- CFG Rescale: `0.0`
+- Steps: `28`
+- Sampler: `FlowMatchEulerDiscreteScheduler`
+- Seed: `42`
+- Resolution: `1024x1024`
+
+
+Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
+
+
+
+
+<Gallery />
+
+The text encoder **was not** trained.
+You may reuse the base model text encoder for inference.
+
+
+## Training settings
+
+- Training epochs: 1
+- Training steps: 4000
+- Learning rate: 1.0
+  - Learning rate schedule: constant
+  - Warmup steps: 0
+- Max grad norm: 1.0
+- Effective batch size: 8
+  - Micro-batch size: 1
+  - Gradient accumulation steps: 1
+  - Number of GPUs: 8
+- Gradient checkpointing: False
+- Prediction type: flow_matching[]
+- Optimizer: prodigy (config=d_coef=2.0,weight_decay=0.01,use_bias_correction=False,schedulefree_c=75,use_orthograd=True)
+- Trainable parameter precision: Pure BF16
+- Base model precision: `no_change`
+- Caption dropout probability: 0.1%
+
+
+
+
+### LyCORIS Config:
+```json
+{
+    "algo": "lokr",
+    "multiplier": 1.0,
+    "factor": 12,
+    "linear_dim": 100000,
+    "linear_alpha": 1,
+    "conv_dim": 0,
+    "conv_alpha": 0,
+    "dora_wd": true,
+    "apply_preset": {
+        "target_module": [
+            "Flux2TransformerBlock",
+            "Flux2SingleTransformerBlock"
+        ]
+    }
+}
+```
+
+
+## Datasets
+
+### dlay-subject-data
+- Repeats: 10
+- Total number of images: ~2152
+- Total number of aspect buckets: 1
+- Resolution: 1.048576 megapixels
+- Cropped: False
+- Crop style: None
+- Crop aspect: None
+- Used for regularisation data: No
+
+
+## Inference
+
+
+```python
+import torch
+from diffusers import DiffusionPipeline
+from lycoris import create_lycoris_from_weights
+
+
+def download_adapter(repo_id: str):
+    import os
+    from huggingface_hub import hf_hub_download
+    adapter_filename = "pytorch_lora_weights.safetensors"
+    cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models'))
+    cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_")
+    path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path)
+    path_to_adapter_file = os.path.join(path_to_adapter, adapter_filename)
+    os.makedirs(path_to_adapter, exist_ok=True)
+    hf_hub_download(
+        repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter
+    )
+
+    return path_to_adapter_file
+    
+model_id = 'black-forest-labs/FLUX.2-dev'
+adapter_repo_id = 'ThatDustyGuy/FLUX.2-DEV-DLAY-LyCORIS'
+adapter_filename = 'pytorch_lora_weights.safetensors'
+adapter_file_path = download_adapter(repo_id=adapter_repo_id)
+pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
+lora_scale = 1.0
+wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer)
+wrapper.merge_to()
+
+prompt = "An astronaut is riding a horse through the jungles of Thailand."
+negative_prompt = 'blurry, cropped, ugly'
+
+## Optional: quantise the model to save on vram.
+## Note: The model was not quantised during training, so it is not necessary to quantise it during inference time.
+#from optimum.quanto import quantize, freeze, qint8
+#quantize(pipeline.transformer, weights=qint8)
+#freeze(pipeline.transformer)
+    
+pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
+model_output = pipeline(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    num_inference_steps=28,
+    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
+    width=1024,
+    height=1024,
+    guidance_scale=4.0,
+).images[0]
+
+model_output.save("output.png", format="PNG")
+
+```
+
+
+
+## Exponential Moving Average (EMA)
+
+SimpleTuner generates a safetensors variant of the EMA weights and a pt file.
+
+The safetensors file is intended to be used for inference, and the pt file is for continuing finetuning.
+
+The EMA model may provide a more well-rounded result, but typically will feel undertrained compared to the full model as it is a running decayed average of the model weights.
+

FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/checkpoint-4000/lycoris_config.json

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+{
+  "algo": "lokr",
+  "multiplier": 1.0,
+  "factor": 12,
+  "linear_dim": 100000,
+  "linear_alpha": 1,
+  "conv_dim": 0,
+  "conv_alpha": 0,
+  "dora_wd": true,
+  "apply_preset": {
+    "target_module": [
+      "Flux2TransformerBlock", 
+      "Flux2SingleTransformerBlock"
+    ]
+  }
+}

FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/checkpoint-4000/simpletuner_config.json

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+{
+    "model_family": "flux2",
+    "model_flavour": "dev",
+    "controlnet": false,
+    "pretrained_model_name_or_path": "black-forest-labs/FLUX.2-dev",
+    "output_dir": "/root/output/FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo",
+    "logging_dir": "/root/output/FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/logs",
+    "model_type": "lora",
+    "seed": 42,
+    "resolution": 1024,
+    "resume_from_checkpoint": "latest",
+    "prediction_type": null,
+    "pretrained_vae_model_name_or_path": "black-forest-labs/FLUX.2-dev",
+    "vae_dtype": "bf16",
+    "vae_cache_ondemand": false,
+    "vae_cache_disable": false,
+    "accelerator_cache_clear_interval": null,
+    "aspect_bucket_rounding": 2,
+    "base_model_precision": "no_change",
+    "text_encoder_1_precision": "no_change",
+    "text_encoder_2_precision": "no_change",
+    "text_encoder_3_precision": "no_change",
+    "text_encoder_4_precision": "no_change",
+    "gradient_checkpointing_interval": null,
+    "offload_during_startup": false,
+    "quantize_via": "accelerator",
+    "quantization_config": null,
+    "wan_force_2_1_time_embedding": false,
+    "fuse_qkv_projections": false,
+    "rescale_betas_zero_snr": false,
+    "control": false,
+    "controlnet_custom_config": null,
+    "controlnet_model_name_or_path": null,
+    "tread_config": null,
+    "pretrained_transformer_model_name_or_path": null,
+    "pretrained_transformer_subfolder": "transformer",
+    "pretrained_unet_model_name_or_path": null,
+    "pretrained_unet_subfolder": "unet",
+    "pretrained_t5_model_name_or_path": null,
+    "revision": null,
+    "variant": null,
+    "base_model_default_dtype": "bf16",
+    "unet_attention_slice": false,
+    "num_train_epochs": 5,
+    "max_train_steps": 12000,
+    "train_batch_size": 1,
+    "learning_rate": 1.0,
+    "optimizer": "prodigy",
+    "lr_scheduler": "constant",
+    "gradient_accumulation_steps": 1,
+    "lr_warmup_steps": 0,
+    "checkpoints_total_limit": 10,
+    "gradient_checkpointing": false,
+    "enable_group_offload": false,
+    "ramtorch": false,
+    "ramtorch_target_modules": null,
+    "ramtorch_text_encoder": false,
+    "ramtorch_vae": false,
+    "ramtorch_controlnet": false,
+    "group_offload_type": "block_level",
+    "group_offload_blocks_per_group": 1,
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+    "push_checkpoints_to_hub": false,
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+        "timeout": "1:30:00"
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+    "base_weight_dtype": "torch.bfloat16",
+    "is_quanto": false,
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+    "is_bnb": false,
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+    "total_steps_remaining_at_start": 11500
+}

FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/checkpoint-4000/training_state-dlay-subject-data.json

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FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/checkpoint-5000/README.md

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+---
+license: other
+base_model: "black-forest-labs/FLUX.2-dev"
+tags:
+  - flux2
+  - flux2-diffusers
+  - text-to-image
+  - image-to-image
+  - diffusers
+  - simpletuner
+  - not-for-all-audiences
+  - lora
+
+  - template:sd-lora
+  - lycoris
+pipeline_tag: text-to-image
+inference: true
+
+---
+
+# FLUX.2-DEV-DLAY-LyCORIS
+
+This is a LyCORIS adapter derived from [black-forest-labs/FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev).
+
+No validation prompt was used during training.
+
+None
+
+
+
+## Validation settings
+- CFG: `4.0`
+- CFG Rescale: `0.0`
+- Steps: `28`
+- Sampler: `FlowMatchEulerDiscreteScheduler`
+- Seed: `42`
+- Resolution: `1024x1024`
+
+
+Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
+
+
+
+
+<Gallery />
+
+The text encoder **was not** trained.
+You may reuse the base model text encoder for inference.
+
+
+## Training settings
+
+- Training epochs: 1
+- Training steps: 5000
+- Learning rate: 1.0
+  - Learning rate schedule: constant
+  - Warmup steps: 0
+- Max grad norm: 1.0
+- Effective batch size: 8
+  - Micro-batch size: 1
+  - Gradient accumulation steps: 1
+  - Number of GPUs: 8
+- Gradient checkpointing: False
+- Prediction type: flow_matching[]
+- Optimizer: prodigy (config=d_coef=2.0,weight_decay=0.01,use_bias_correction=False,schedulefree_c=75,use_orthograd=True)
+- Trainable parameter precision: Pure BF16
+- Base model precision: `no_change`
+- Caption dropout probability: 0.1%
+
+
+
+
+### LyCORIS Config:
+```json
+{
+    "algo": "lokr",
+    "multiplier": 1.0,
+    "factor": 12,
+    "linear_dim": 100000,
+    "linear_alpha": 1,
+    "conv_dim": 0,
+    "conv_alpha": 0,
+    "dora_wd": true,
+    "apply_preset": {
+        "target_module": [
+            "Flux2TransformerBlock",
+            "Flux2SingleTransformerBlock"
+        ]
+    }
+}
+```
+
+
+## Datasets
+
+### dlay-subject-data
+- Repeats: 10
+- Total number of images: ~2152
+- Total number of aspect buckets: 1
+- Resolution: 1.048576 megapixels
+- Cropped: False
+- Crop style: None
+- Crop aspect: None
+- Used for regularisation data: No
+
+
+## Inference
+
+
+```python
+import torch
+from diffusers import DiffusionPipeline
+from lycoris import create_lycoris_from_weights
+
+
+def download_adapter(repo_id: str):
+    import os
+    from huggingface_hub import hf_hub_download
+    adapter_filename = "pytorch_lora_weights.safetensors"
+    cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models'))
+    cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_")
+    path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path)
+    path_to_adapter_file = os.path.join(path_to_adapter, adapter_filename)
+    os.makedirs(path_to_adapter, exist_ok=True)
+    hf_hub_download(
+        repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter
+    )
+
+    return path_to_adapter_file
+    
+model_id = 'black-forest-labs/FLUX.2-dev'
+adapter_repo_id = 'ThatDustyGuy/FLUX.2-DEV-DLAY-LyCORIS'
+adapter_filename = 'pytorch_lora_weights.safetensors'
+adapter_file_path = download_adapter(repo_id=adapter_repo_id)
+pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
+lora_scale = 1.0
+wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer)
+wrapper.merge_to()
+
+prompt = "An astronaut is riding a horse through the jungles of Thailand."
+negative_prompt = 'blurry, cropped, ugly'
+
+## Optional: quantise the model to save on vram.
+## Note: The model was not quantised during training, so it is not necessary to quantise it during inference time.
+#from optimum.quanto import quantize, freeze, qint8
+#quantize(pipeline.transformer, weights=qint8)
+#freeze(pipeline.transformer)
+    
+pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
+model_output = pipeline(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    num_inference_steps=28,
+    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
+    width=1024,
+    height=1024,
+    guidance_scale=4.0,
+).images[0]
+
+model_output.save("output.png", format="PNG")
+
+```
+
+
+
+## Exponential Moving Average (EMA)
+
+SimpleTuner generates a safetensors variant of the EMA weights and a pt file.
+
+The safetensors file is intended to be used for inference, and the pt file is for continuing finetuning.
+
+The EMA model may provide a more well-rounded result, but typically will feel undertrained compared to the full model as it is a running decayed average of the model weights.
+

FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/checkpoint-5000/lycoris_config.json

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+{
+  "algo": "lokr",
+  "multiplier": 1.0,
+  "factor": 12,
+  "linear_dim": 100000,
+  "linear_alpha": 1,
+  "conv_dim": 0,
+  "conv_alpha": 0,
+  "dora_wd": true,
+  "apply_preset": {
+    "target_module": [
+      "Flux2TransformerBlock", 
+      "Flux2SingleTransformerBlock"
+    ]
+  }
+}

FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/checkpoint-5000/simpletuner_config.json

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+{
+    "model_family": "flux2",
+    "model_flavour": "dev",
+    "controlnet": false,
+    "pretrained_model_name_or_path": "black-forest-labs/FLUX.2-dev",
+    "output_dir": "/root/output/FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo",
+    "logging_dir": "/root/output/FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/logs",
+    "model_type": "lora",
+    "seed": 42,
+    "resolution": 1024,
+    "resume_from_checkpoint": "latest",
+    "prediction_type": null,
+    "pretrained_vae_model_name_or_path": "black-forest-labs/FLUX.2-dev",
+    "vae_dtype": "bf16",
+    "vae_cache_ondemand": false,
+    "vae_cache_disable": false,
+    "accelerator_cache_clear_interval": null,
+    "aspect_bucket_rounding": 2,
+    "base_model_precision": "no_change",
+    "text_encoder_1_precision": "no_change",
+    "text_encoder_2_precision": "no_change",
+    "text_encoder_3_precision": "no_change",
+    "text_encoder_4_precision": "no_change",
+    "gradient_checkpointing_interval": null,
+    "offload_during_startup": false,
+    "quantize_via": "accelerator",
+    "quantization_config": null,
+    "wan_force_2_1_time_embedding": false,
+    "fuse_qkv_projections": false,
+    "rescale_betas_zero_snr": false,
+    "control": false,
+    "controlnet_custom_config": null,
+    "controlnet_model_name_or_path": null,
+    "tread_config": null,
+    "pretrained_transformer_model_name_or_path": null,
+    "pretrained_transformer_subfolder": "transformer",
+    "pretrained_unet_model_name_or_path": null,
+    "pretrained_unet_subfolder": "unet",
+    "pretrained_t5_model_name_or_path": null,
+    "revision": null,
+    "variant": null,
+    "base_model_default_dtype": "bf16",
+    "unet_attention_slice": false,
+    "num_train_epochs": 5,
+    "max_train_steps": 12000,
+    "train_batch_size": 1,
+    "learning_rate": 1.0,
+    "optimizer": "prodigy",
+    "lr_scheduler": "constant",
+    "gradient_accumulation_steps": 1,
+    "lr_warmup_steps": 0,
+    "checkpoints_total_limit": 10,
+    "gradient_checkpointing": false,
+    "enable_group_offload": false,
+    "ramtorch": false,
+    "ramtorch_target_modules": null,
+    "ramtorch_text_encoder": false,
+    "ramtorch_vae": false,
+    "ramtorch_controlnet": false,
+    "group_offload_type": "block_level",
+    "group_offload_blocks_per_group": 1,
+    "group_offload_use_stream": false,
+    "group_offload_to_disk_path": "",
+    "group_offload_text_encoder": false,
+    "group_offload_vae": false,
+    "offload_during_save": false,
+    "musubi_blocks_to_swap": 0,
+    "musubi_block_swap_device": "cpu",
+    "enable_chunked_feed_forward": false,
+    "feed_forward_chunk_size": null,
+    "train_text_encoder": false,
+    "text_encoder_lr": null,
+    "lyrics_embedder_train": false,
+    "lyrics_embedder_optimizer": null,
+    "lyrics_embedder_lr": null,
+    "lyrics_embedder_lr_scheduler": null,
+    "lr_num_cycles": 1,
+    "lr_power": 0.8,
+    "use_soft_min_snr": false,
+    "use_ema": true,
+    "ema_device": "cpu",
+    "ema_cpu_only": false,
+    "ema_update_interval": 10,
+    "ema_foreach_disable": false,
+    "ema_decay": 0.9999,
+    "lora_rank": 16,
+    "lora_alpha": null,
+    "lora_type": "lycoris",
+    "slider_lora_target": false,
+    "lora_dropout": 0.1,
+    "lora_init_type": "default",
+    "peft_lora_mode": "standard",
+    "singlora_ramp_up_steps": 0,
+    "init_lora": null,
+    "assistant_lora_path": null,
+    "assistant_lora_strength": 1.0,
+    "assistant_lora_inference_strength": 0.0,
+    "disable_assistant_lora": false,
+    "lora_format": "diffusers",
+    "lycoris_config": "/root/SimpleTuner/config/FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2/lycoris_config_full.json",
+    "init_lokr_norm": null,
+    "flux_lora_target": "all",
+    "acestep_lora_target": "attn_qkv+linear_qkv",
+    "use_dora": false,
+    "resolution_type": "pixel_area",
+    "data_backend_config": "/root/SimpleTuner/config/FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2/multidatabackend.json",
+    "caption_strategy": "filename",
+    "conditioning_multidataset_sampling": "random",
+    "instance_prompt": null,
+    "parquet_caption_column": null,
+    "parquet_filename_column": null,
+    "ignore_missing_files": false,
+    "vae_cache_scan_behaviour": "recreate",
+    "vae_enable_slicing": false,
+    "vae_enable_tiling": false,
+    "vae_enable_patch_conv": false,
+    "vae_enable_temporal_roll": false,
+    "vae_batch_size": 4,
+    "max_upscale_threshold": null,
+    "caption_dropout_probability": 0.1,
+    "tokenizer_max_length": 512,
+    "audio_max_duration_seconds": null,
+    "audio_min_duration_seconds": null,
+    "audio_channels": 1,
+    "audio_duration_interval": 3.0,
+    "audio_truncation_mode": "beginning",
+    "validation_step_interval": 500,
+    "validation_epoch_interval": null,
+    "disable_benchmark": false,
+    "validation_preview": false,
+    "validation_preview_steps": 1,
+    "validation_prompt": null,
+    "validation_lyrics": null,
+    "validation_audio_duration": 30.0,
+    "num_validation_images": 1,
+    "num_eval_images": 4,
+    "eval_steps_interval": 1000,
+    "eval_epoch_interval": null,
+    "eval_timesteps": 28,
+    "eval_dataset_pooling": false,
+    "evaluation_type": "none",
+    "pretrained_evaluation_model_name_or_path": "openai/clip-vit-large-patch14-336",
+    "validation_guidance": 4.0,
+    "validation_num_inference_steps": 28,
+    "validation_on_startup": false,
+    "validation_method": "simpletuner-local",
+    "validation_external_script": null,
+    "validation_external_background": false,
+    "validation_using_datasets": null,
+    "validation_guidance_real": 1.0,
+    "validation_no_cfg_until_timestep": 2,
+    "validation_negative_prompt": "blurry, cropped, ugly",
+    "validation_randomize": false,
+    "validation_seed": 42,
+    "validation_multigpu": "single-gpu",
+    "validation_disable": false,
+    "validation_prompt_library": false,
+    "user_prompt_library": "/root/SimpleTuner/config/FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2/user_prompt_library.json",
+    "eval_dataset_id": null,
+    "validation_stitch_input_location": "left",
+    "validation_guidance_rescale": 0.0,
+    "validation_disable_unconditional": true,
+    "validation_guidance_skip_layers": null,
+    "validation_guidance_skip_layers_start": 0.01,
+    "validation_guidance_skip_layers_stop": 0.2,
+    "validation_guidance_skip_scale": 2.8,
+    "validation_lycoris_strength": 1.0,
+    "validation_noise_scheduler": null,
+    "validation_num_video_frames": null,
+    "validation_resolution": "1024x1024",
+    "validation_seed_source": "cpu",
+    "validation_adapter_path": null,
+    "validation_adapter_name": null,
+    "validation_adapter_strength": 1.0,
+    "validation_adapter_mode": "adapter_only",
+    "validation_adapter_config": null,
+    "i_know_what_i_am_doing": false,
+    "diff2flow_enabled": false,
+    "diff2flow_loss": false,
+    "scheduled_sampling_max_step_offset": 0,
+    "scheduled_sampling_strategy": "uniform",
+    "scheduled_sampling_probability": 0.0,
+    "scheduled_sampling_prob_start": 0.0,
+    "scheduled_sampling_prob_end": 0.5,
+    "scheduled_sampling_ramp_steps": 0,
+    "scheduled_sampling_start_step": 0,
+    "scheduled_sampling_ramp_shape": "linear",
+    "scheduled_sampling_sampler": "unipc",
+    "scheduled_sampling_order": 2,
+    "scheduled_sampling_reflexflow": null,
+    "scheduled_sampling_reflexflow_alpha": 1.0,
+    "scheduled_sampling_reflexflow_beta1": 10.0,
+    "scheduled_sampling_reflexflow_beta2": 1.0,
+    "flow_sigmoid_scale": 1.0,
+    "flux_fast_schedule": false,
+    "flow_use_uniform_schedule": false,
+    "flow_use_beta_schedule": false,
+    "flow_beta_schedule_alpha": 2.0,
+    "flow_beta_schedule_beta": 2.0,
+    "flow_schedule_shift": 3.0,
+    "flow_schedule_auto_shift": false,
+    "flux_guidance_mode": "constant",
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+    "flux_guidance_value": 1.0,
+    "flux_guidance_min": 0.0,
+    "flux_guidance_max": 4.0,
+    "t5_padding": "unmodified",
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+    "use_deepspeed_scheduler": false,
+    "base_weight_dtype": "torch.bfloat16",
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+    "is_torchao": false,
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+}

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FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/checkpoint-5000/training_state.json

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+{"global_step": 5000, "epoch_step": 5001, "epoch": 2, "exhausted_backends": [], "repeats": {"dlay-subject-data": 7}}

FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/checkpoint-5500/README.md

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+---
+license: other
+base_model: "black-forest-labs/FLUX.2-dev"
+tags:
+  - flux2
+  - flux2-diffusers
+  - text-to-image
+  - image-to-image
+  - diffusers
+  - simpletuner
+  - not-for-all-audiences
+  - lora
+
+  - template:sd-lora
+  - lycoris
+pipeline_tag: text-to-image
+inference: true
+
+---
+
+# FLUX.2-DEV-DLAY-LyCORIS
+
+This is a LyCORIS adapter derived from [black-forest-labs/FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev).
+
+No validation prompt was used during training.
+
+None
+
+
+
+## Validation settings
+- CFG: `4.0`
+- CFG Rescale: `0.0`
+- Steps: `28`
+- Sampler: `FlowMatchEulerDiscreteScheduler`
+- Seed: `42`
+- Resolution: `1024x1024`
+
+
+Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
+
+
+
+
+<Gallery />
+
+The text encoder **was not** trained.
+You may reuse the base model text encoder for inference.
+
+
+## Training settings
+
+- Training epochs: 1
+- Training steps: 5500
+- Learning rate: 1.0
+  - Learning rate schedule: constant
+  - Warmup steps: 0
+- Max grad norm: 1.0
+- Effective batch size: 8
+  - Micro-batch size: 1
+  - Gradient accumulation steps: 1
+  - Number of GPUs: 8
+- Gradient checkpointing: False
+- Prediction type: flow_matching[]
+- Optimizer: prodigy (config=d_coef=2.0,weight_decay=0.01,use_bias_correction=False,schedulefree_c=75,use_orthograd=True)
+- Trainable parameter precision: Pure BF16
+- Base model precision: `no_change`
+- Caption dropout probability: 0.1%
+
+
+
+
+### LyCORIS Config:
+```json
+{
+    "algo": "lokr",
+    "multiplier": 1.0,
+    "factor": 12,
+    "linear_dim": 100000,
+    "linear_alpha": 1,
+    "conv_dim": 0,
+    "conv_alpha": 0,
+    "dora_wd": true,
+    "apply_preset": {
+        "target_module": [
+            "Flux2TransformerBlock",
+            "Flux2SingleTransformerBlock"
+        ]
+    }
+}
+```
+
+
+## Datasets
+
+### dlay-subject-data
+- Repeats: 10
+- Total number of images: ~2152
+- Total number of aspect buckets: 1
+- Resolution: 1.048576 megapixels
+- Cropped: False
+- Crop style: None
+- Crop aspect: None
+- Used for regularisation data: No
+
+
+## Inference
+
+
+```python
+import torch
+from diffusers import DiffusionPipeline
+from lycoris import create_lycoris_from_weights
+
+
+def download_adapter(repo_id: str):
+    import os
+    from huggingface_hub import hf_hub_download
+    adapter_filename = "pytorch_lora_weights.safetensors"
+    cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models'))
+    cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_")
+    path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path)
+    path_to_adapter_file = os.path.join(path_to_adapter, adapter_filename)
+    os.makedirs(path_to_adapter, exist_ok=True)
+    hf_hub_download(
+        repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter
+    )
+
+    return path_to_adapter_file
+    
+model_id = 'black-forest-labs/FLUX.2-dev'
+adapter_repo_id = 'ThatDustyGuy/FLUX.2-DEV-DLAY-LyCORIS'
+adapter_filename = 'pytorch_lora_weights.safetensors'
+adapter_file_path = download_adapter(repo_id=adapter_repo_id)
+pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
+lora_scale = 1.0
+wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer)
+wrapper.merge_to()
+
+prompt = "An astronaut is riding a horse through the jungles of Thailand."
+negative_prompt = 'blurry, cropped, ugly'
+
+## Optional: quantise the model to save on vram.
+## Note: The model was not quantised during training, so it is not necessary to quantise it during inference time.
+#from optimum.quanto import quantize, freeze, qint8
+#quantize(pipeline.transformer, weights=qint8)
+#freeze(pipeline.transformer)
+    
+pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
+model_output = pipeline(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    num_inference_steps=28,
+    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
+    width=1024,
+    height=1024,
+    guidance_scale=4.0,
+).images[0]
+
+model_output.save("output.png", format="PNG")
+
+```
+
+
+
+## Exponential Moving Average (EMA)
+
+SimpleTuner generates a safetensors variant of the EMA weights and a pt file.
+
+The safetensors file is intended to be used for inference, and the pt file is for continuing finetuning.
+
+The EMA model may provide a more well-rounded result, but typically will feel undertrained compared to the full model as it is a running decayed average of the model weights.
+

FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/checkpoint-5500/lycoris_config.json

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+{
+  "algo": "lokr",
+  "multiplier": 1.0,
+  "factor": 12,
+  "linear_dim": 100000,
+  "linear_alpha": 1,
+  "conv_dim": 0,
+  "conv_alpha": 0,
+  "dora_wd": true,
+  "apply_preset": {
+    "target_module": [
+      "Flux2TransformerBlock", 
+      "Flux2SingleTransformerBlock"
+    ]
+  }
+}

FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/checkpoint-5500/simpletuner_config.json

@@ -0,0 +1,418 @@
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+    "resume_from_checkpoint": "latest",
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+    "quantize_via": "accelerator",
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+    "group_offload_text_encoder": false,
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+    "lora_alpha": null,
+    "lora_type": "lycoris",
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+    "lora_dropout": 0.1,
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+    "lora_format": "diffusers",
+    "lycoris_config": "/root/SimpleTuner/config/FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2/lycoris_config_full.json",
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+    "flux_lora_target": "all",
+    "acestep_lora_target": "attn_qkv+linear_qkv",
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+    "resolution_type": "pixel_area",
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+    "validation_prompt_library": false,
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+    "eval_dataset_id": null,
+    "validation_stitch_input_location": "left",
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+    "validation_disable_unconditional": true,
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+    "validation_resolution": "1024x1024",
+    "validation_seed_source": "cpu",
+    "validation_adapter_path": null,
+    "validation_adapter_name": null,
+    "validation_adapter_strength": 1.0,
+    "validation_adapter_mode": "adapter_only",
+    "validation_adapter_config": null,
+    "i_know_what_i_am_doing": false,
+    "diff2flow_enabled": false,
+    "diff2flow_loss": false,
+    "scheduled_sampling_max_step_offset": 0,
+    "scheduled_sampling_strategy": "uniform",
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+    "scheduled_sampling_ramp_shape": "linear",
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+    "scheduled_sampling_reflexflow": null,
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+    "delete_unwanted_images": false,
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+    "disable_bucket_pruning": false,
+    "allow_dataset_oversubscription": false,
+    "disable_segmented_timestep_sampling": false,
+    "preserve_data_backend_cache": false,
+    "override_dataset_config": false,
+    "cache_dir": "/root/output/FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/cache",
+    "cache_dir_text": "cache",
+    "cache_dir_vae": "",
+    "compress_disk_cache": true,
+    "aspect_bucket_disable_rebuild": false,
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+    "fsdp_cpu_ram_efficient_loading": false,
+    "fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
+    "fsdp_limit_all_gathers": true,
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+    "crepa_adjacent_tau": 1.0,
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+    "crepa_normalize_by_frames": true,
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+    "optimizer_config": "d_coef=2.0,weight_decay=0.01,use_bias_correction=False,schedulefree_c=75,use_orthograd=True",
+    "grad_clip_method": "norm",
+    "optimizer_offload_gradients": false,
+    "fuse_optimizer": false,
+    "optimizer_release_gradients": false,
+    "push_to_hub": true,
+    "publishing_config": null,
+    "post_checkpoint_script": null,
+    "post_upload_script": null,
+    "push_checkpoints_to_hub": false,
+    "push_to_hub_background": false,
+    "hub_model_id": "FLUX.2-DEV-DLAY-LyCORIS",
+    "model_card_private": true,
+    "model_card_safe_for_work": false,
+    "model_card_note": null,
+    "report_to": "wandb",
+    "custom_tracker": null,
+    "checkpoint_step_interval": 500,
+    "checkpoint_epoch_interval": null,
+    "checkpointing_rolling_steps": 0,
+    "checkpointing_use_tempdir": false,
+    "checkpoints_rolling_total_limit": 1,
+    "tracker_run_name": "dlay-persona-prodigy-bf16-8xB300-V2-dynamo",
+    "tracker_project_name": "FLUX.2-DEV-DLAY-Persona",
+    "tracker_image_layout": "gallery",
+    "enable_watermark": false,
+    "framerate": null,
+    "seed_for_each_device": true,
+    "snr_weight": 1.0,
+    "webhook_config": null,
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+    "distillation_method": null,
+    "distillation_config": null,
+    "ema_validation": "ema_only",
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+    "ltx_train_mode": "i2v",
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+    "ltx_partial_noise_fraction": 0.05,
+    "ltx_protect_first_frame": false,
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+    "offset_noise": false,
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+    "sana_complex_human_instruction": "Given a user prompt, generate an 'Enhanced prompt' that provides detailed visual descriptions suitable for image generation. Evaluate the level of detail in the user prompt:\n- If the prompt is simple, focus on adding specifics about colors, shapes, sizes, textures, and spatial relationships to create vivid and concrete scenes.\n- Do not add detail that is not in the original prompt! Do not mention colors if the User Prompt does not!\n- If the prompt is already detailed, refine and enhance the existing details slightly without overcomplicating.\n- If the prompt contains explicit material, do not remove this information from the output, which would damage the performance of downstream classifiers.\nHere are examples of how to transform or refine prompts:\n- User Prompt: A cat sleeping -> Enhanced: A cat sleeping peacefully, showcasing the joy of pet ownership. Cute floof kitty cat gatto.\n- User Prompt: A busy city street -> Enhanced: A bustling city street scene featuring a crowd of people.\nPlease generate only the enhanced description for the prompt below and avoid including any additional commentary or evaluations:\nUser Prompt: ",
+    "vae_path": "black-forest-labs/FLUX.2-dev",
+    "accelerator_project_config": {
+        "project_dir": "/root/output/FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo",
+        "logging_dir": "/root/output/FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/logs",
+        "automatic_checkpoint_naming": false,
+        "total_limit": null,
+        "iteration": 10,
+        "save_on_each_node": false
+    },
+    "process_group_kwargs": {
+        "backend": "nccl",
+        "init_method": null,
+        "timeout": "1:30:00"
+    },
+    "is_quantized": false,
+    "weight_dtype": "torch.bfloat16",
+    "disable_accelerator": false,
+    "checkpointing_steps": 500,
+    "use_fsdp": false,
+    "model_type_label": "Flux.2",
+    "use_deepspeed_optimizer": false,
+    "use_deepspeed_scheduler": false,
+    "base_weight_dtype": "torch.bfloat16",
+    "is_quanto": false,
+    "is_torchao": false,
+    "is_bnb": false,
+    "pipeline_quantization": false,
+    "pipeline_quantization_base": false,
+    "flow_matching": true,
+    "enable_adamw_bf16": true,
+    "overrode_max_train_steps": false,
+    "total_num_batches": 2959,
+    "num_update_steps_per_epoch": 2959,
+    "total_batch_size": 8,
+    "is_schedulefree": true,
+    "is_lr_scheduler_disabled": false,
+    "total_steps_remaining_at_start": 11500
+}

FLUX.2-DEV-DLAY-Persona-8xB300-prodigy-V2-dynamo/checkpoint-5500/training_state-dlay-subject-data.json

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FLUX.2-DEV-DLAY-Persona-cudnn-debug-1step/README.md

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+---
+license: other
+base_model: "black-forest-labs/FLUX.2-dev"
+tags:
+  - flux2
+  - flux2-diffusers
+  - text-to-image
+  - image-to-image
+  - diffusers
+  - simpletuner
+  - not-for-all-audiences
+  - lora
+
+  - template:sd-lora
+  - lycoris
+pipeline_tag: text-to-image
+inference: true
+widget:
+- text: 'unconditional (blank prompt)'
+  parameters:
+    negative_prompt: 'blurry, cropped, ugly'
+  output:
+    url: ./assets/image_0_0.png
+---
+
+# simpletuner
+
+This is a LyCORIS adapter derived from [black-forest-labs/FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev).
+
+No validation prompt was used during training.
+
+None
+
+
+
+## Validation settings
+- CFG: `7.5`
+- CFG Rescale: `0.0`
+- Steps: `30`
+- Sampler: `FlowMatchEulerDiscreteScheduler`
+- Seed: `None`
+- Resolution: `256`
+
+
+Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
+
+You can find some example images in the following gallery:
+
+
+<Gallery />
+
+The text encoder **was not** trained.
+You may reuse the base model text encoder for inference.
+
+
+## Training settings
+
+- Training epochs: 0
+- Training steps: 1
+- Learning rate: 1.0
+  - Learning rate schedule: constant
+  - Warmup steps: 0
+- Max grad norm: 1.0
+- Effective batch size: 1
+  - Micro-batch size: 1
+  - Gradient accumulation steps: 1
+  - Number of GPUs: 1
+- Gradient checkpointing: False
+- Prediction type: flow_matching[]
+- Optimizer: prodigy (config=d_coef=2.0,weight_decay=0.01,use_bias_correction=False,schedulefree_c=75,use_orthograd=True)
+- Trainable parameter precision: Pure BF16
+- Base model precision: `no_change`
+- Caption dropout probability: 0.1%
+
+
+
+
+### LyCORIS Config:
+```json
+{
+    "algo": "lokr",
+    "multiplier": 1.0,
+    "factor": 12,
+    "linear_dim": 100000,
+    "linear_alpha": 1,
+    "conv_dim": 0,
+    "conv_alpha": 0,
+    "dora_wd": true,
+    "apply_preset": {
+        "target_module": [
+            "Flux2TransformerBlock",
+            "Flux2SingleTransformerBlock"
+        ]
+    }
+}
+```
+
+
+## Datasets
+
+### dlay-subject-data
+- Repeats: 10
+- Total number of images: 269
+- Total number of aspect buckets: 1
+- Resolution: 1.048576 megapixels
+- Cropped: False
+- Crop style: None
+- Crop aspect: None
+- Used for regularisation data: No
+
+
+## Inference
+
+
+```python
+import torch
+from diffusers import DiffusionPipeline
+from lycoris import create_lycoris_from_weights
+
+
+def download_adapter(repo_id: str):
+    import os
+    from huggingface_hub import hf_hub_download
+    adapter_filename = "pytorch_lora_weights.safetensors"
+    cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models'))
+    cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_")
+    path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path)
+    path_to_adapter_file = os.path.join(path_to_adapter, adapter_filename)
+    os.makedirs(path_to_adapter, exist_ok=True)
+    hf_hub_download(
+        repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter
+    )
+
+    return path_to_adapter_file
+    
+model_id = 'black-forest-labs/FLUX.2-dev'
+adapter_repo_id = 'simpletuner'
+adapter_filename = 'pytorch_lora_weights.safetensors'
+adapter_file_path = download_adapter(repo_id=adapter_repo_id)
+pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
+lora_scale = 1.0
+wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer)
+wrapper.merge_to()
+
+prompt = "An astronaut is riding a horse through the jungles of Thailand."
+negative_prompt = 'blurry, cropped, ugly'
+
+## Optional: quantise the model to save on vram.
+## Note: The model was not quantised during training, so it is not necessary to quantise it during inference time.
+#from optimum.quanto import quantize, freeze, qint8
+#quantize(pipeline.transformer, weights=qint8)
+#freeze(pipeline.transformer)
+    
+pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
+model_output = pipeline(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    num_inference_steps=30,
+    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
+    width=256,
+    height=256,
+    guidance_scale=7.5,
+).images[0]
+
+model_output.save("output.png", format="PNG")
+
+```
+
+
+

FLUX.2-DEV-DLAY-Persona-cudnn-debug-1step/all_image_files_dlay-subject-data.json

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FLUX.2-DEV-DLAY-Persona-cudnn-debug-1step/lycoris_config.json

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FLUX.2-DEV-DLAY-Persona-cudnn-debug-1step/simpletuner_config.json

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FLUX.2-DEV-DLAY-Persona-cudnn-debug-benchmark-1step/lycoris_config.json

@@ -0,0 +1,16 @@
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FLUX.2-DEV-DLAY-Persona-cudnn-debug-benchmark-1step/simpletuner_config.json

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FLUX.2-DEV-DLAY-Persona-native-efficient-debug-5step/README.md

@@ -0,0 +1,170 @@
+---
+license: other
+base_model: "black-forest-labs/FLUX.2-dev"
+tags:
+  - flux2
+  - flux2-diffusers
+  - text-to-image
+  - image-to-image
+  - diffusers
+  - simpletuner
+  - not-for-all-audiences
+  - lora
+
+  - template:sd-lora
+  - lycoris
+pipeline_tag: text-to-image
+inference: true
+widget:
+- text: 'unconditional (blank prompt)'
+  parameters:
+    negative_prompt: 'blurry, cropped, ugly'
+  output:
+    url: ./assets/image_0_0.png
+---
+
+# simpletuner
+
+This is a LyCORIS adapter derived from [black-forest-labs/FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev).
+
+No validation prompt was used during training.
+
+None
+
+
+
+## Validation settings
+- CFG: `7.5`
+- CFG Rescale: `0.0`
+- Steps: `30`
+- Sampler: `FlowMatchEulerDiscreteScheduler`
+- Seed: `None`
+- Resolution: `256`
+
+
+Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
+
+You can find some example images in the following gallery:
+
+
+<Gallery />
+
+The text encoder **was not** trained.
+You may reuse the base model text encoder for inference.
+
+
+## Training settings
+
+- Training epochs: 0
+- Training steps: 5
+- Learning rate: 1.0
+  - Learning rate schedule: constant
+  - Warmup steps: 0
+- Max grad value: 2.0
+- Effective batch size: 1
+  - Micro-batch size: 1
+  - Gradient accumulation steps: 1
+  - Number of GPUs: 1
+- Gradient checkpointing: False
+- Prediction type: flow_matching[]
+- Optimizer: prodigy (config=d_coef=2.0,weight_decay=0.01,use_bias_correction=False,schedulefree_c=75,use_orthograd=True)
+- Trainable parameter precision: Pure BF16
+- Base model precision: `no_change`
+- Caption dropout probability: 0.1%
+
+
+
+
+### LyCORIS Config:
+```json
+{
+    "algo": "lokr",
+    "multiplier": 1.0,
+    "factor": 12,
+    "linear_dim": 100000,
+    "linear_alpha": 1,
+    "conv_dim": 0,
+    "conv_alpha": 0,
+    "dora_wd": true,
+    "apply_preset": {
+        "target_module": [
+            "Flux2TransformerBlock",
+            "Flux2SingleTransformerBlock"
+        ]
+    }
+}
+```
+
+
+## Datasets
+
+### dlay-subject-data
+- Repeats: 10
+- Total number of images: 269
+- Total number of aspect buckets: 1
+- Resolution: 1.048576 megapixels
+- Cropped: False
+- Crop style: None
+- Crop aspect: None
+- Used for regularisation data: No
+
+
+## Inference
+
+
+```python
+import torch
+from diffusers import DiffusionPipeline
+from lycoris import create_lycoris_from_weights
+
+
+def download_adapter(repo_id: str):
+    import os
+    from huggingface_hub import hf_hub_download
+    adapter_filename = "pytorch_lora_weights.safetensors"
+    cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models'))
+    cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_")
+    path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path)
+    path_to_adapter_file = os.path.join(path_to_adapter, adapter_filename)
+    os.makedirs(path_to_adapter, exist_ok=True)
+    hf_hub_download(
+        repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter
+    )
+
+    return path_to_adapter_file
+    
+model_id = 'black-forest-labs/FLUX.2-dev'
+adapter_repo_id = 'simpletuner'
+adapter_filename = 'pytorch_lora_weights.safetensors'
+adapter_file_path = download_adapter(repo_id=adapter_repo_id)
+pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
+lora_scale = 1.0
+wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer)
+wrapper.merge_to()
+
+prompt = "An astronaut is riding a horse through the jungles of Thailand."
+negative_prompt = 'blurry, cropped, ugly'
+
+## Optional: quantise the model to save on vram.
+## Note: The model was not quantised during training, so it is not necessary to quantise it during inference time.
+#from optimum.quanto import quantize, freeze, qint8
+#quantize(pipeline.transformer, weights=qint8)
+#freeze(pipeline.transformer)
+    
+pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
+model_output = pipeline(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    num_inference_steps=30,
+    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
+    width=256,
+    height=256,
+    guidance_scale=7.5,
+).images[0]
+
+model_output.save("output.png", format="PNG")
+
+```
+
+
+

FLUX.2-DEV-DLAY-Persona-native-efficient-debug-5step/all_image_files_dlay-subject-data.json

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FLUX.2-DEV-DLAY-Persona-native-efficient-debug-5step/lycoris_config.json

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FLUX.2-DEV-DLAY-Persona-native-efficient-debug-5step/simpletuner_config.json

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+    "accelerator_project_config": {
+        "project_dir": "/root/output/FLUX.2-DEV-DLAY-Persona-native-efficient-debug-5step",
+        "logging_dir": "/root/output/FLUX.2-DEV-DLAY-Persona-native-efficient-debug-5step/logs",
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+        "total_limit": null,
+        "iteration": 0,
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+    },
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+        "init_method": null,
+        "timeout": "1:30:00"
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+    "is_quantized": false,
+    "weight_dtype": "torch.bfloat16",
+    "disable_accelerator": false,
+    "use_fsdp": false,
+    "model_type_label": "Flux.2",
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+    "use_deepspeed_scheduler": false,
+    "base_weight_dtype": "torch.bfloat16",
+    "is_quanto": false,
+    "is_torchao": false,
+    "is_bnb": false,
+    "pipeline_quantization": false,
+    "pipeline_quantization_base": false,
+    "flow_matching": true,
+    "enable_adamw_bf16": true,
+    "overrode_max_train_steps": false,
+    "total_num_batches": 2959,
+    "num_update_steps_per_epoch": 2959,
+    "total_batch_size": 1,
+    "is_schedulefree": true,
+    "is_lr_scheduler_disabled": false,
+    "total_steps_remaining_at_start": 5
+}

wandb/run-20251215_152700-2a12881713e36559822624a641f93a53/files/output.log

@@ -0,0 +1,6 @@
+wandb: wandb.init() called while a run is active and reinit is set to 'default', so returning the previous run.
+wandb: wandb.init() called while a run is active and reinit is set to 'default', so returning the previous run.
+Epoch 1/9, Steps:   0%|                   | 2/12000 [00:15<25:42:18,  7.71s/it, grad_norm=0.00105, lr=1e-6, step_loss=0.0311]wandb: WARNING You are mutating a Table with log_mode='IMMUTABLE' that has been logged already. Subsequent log() calls will have no effect. Set log_mode='MUTABLE' to enable re-logging after mutations
+Epoch 1/9, Steps:   2%|▎              | 201/12000 [19:13<18:48:19,  5.74s/it, grad_norm=0.00034, lr=4.78e-5, step_loss=0.273]
+[RANK 0] 2025-12-15 15:46:15,687 [INFO] Unloading all model components...
+[RANK 0] 2025-12-15 15:46:17,411 [INFO] Model components unloaded successfully.

wandb/run-20251215_184919-135f322fb183928bf4f8c7b1a6c154ea/logs/.ipynb_checkpoints/debug-checkpoint.log

@@ -0,0 +1,11 @@
+2025-12-15 18:49:19,006 INFO    MainThread:1281414 [logging.py:_log():419] Logging user logs to /root/output/wandb/run-20251215_184919-135f322fb183928bf4f8c7b1a6c154ea/logs/debug.log
+2025-12-15 18:49:19,006 INFO    MainThread:1281414 [logging.py:_log():419] Logging internal logs to /root/output/wandb/run-20251215_184919-135f322fb183928bf4f8c7b1a6c154ea/logs/debug-internal.log
+2025-12-15 18:49:19,006 INFO    MainThread:1281414 [logging.py:_log():419] calling init triggers
+2025-12-15 18:49:19,006 INFO    MainThread:1281414 [logging.py:_log():419] wandb.init called with sweep_config: {}
+config: {'_wandb': {}}
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+2025-12-15 18:49:24,043 INFO    MainThread:1281414 [logging.py:_log():419] config_cb ('_wandb', 'visualize', 'timesteps_scatter') {'panel_type': 'Vega2', 'panel_config': {'panelDefId': 'wandb/scatter/v0', 'fieldSettings': {'x': 'global_step', 'y': 'timestep'}, 'stringSettings': {'title': 'Timestep distribution by step'}, 'transform': {'name': 'tableWithLeafColNames'}, 'userQuery': {'queryFields': [{'name': 'runSets', 'args': [{'name': 'runSets', 'value': '${runSets}'}], 'fields': [{'name': 'id', 'fields': []}, {'name': 'name', 'fields': []}, {'name': '_defaultColorIndex', 'fields': []}, {'name': 'summaryTable', 'args': [{'name': 'tableKey', 'value': 'timesteps_scatter_table'}], 'fields': []}]}]}}} None
+2025-12-15 18:49:26,210 INFO    MainThread:1281414 [logging.py:_log():419] config_cb ('_wandb', 'visualize', 'timesteps_scatter') {'panel_type': 'Vega2', 'panel_config': {'panelDefId': 'wandb/scatter/v0', 'fieldSettings': {'x': 'global_step', 'y': 'timestep'}, 'stringSettings': {'title': 'Timestep distribution by step'}, 'transform': {'name': 'tableWithLeafColNames'}, 'userQuery': {'queryFields': [{'name': 'runSets', 'args': [{'name': 'runSets', 'value': '${runSets}'}], 'fields': [{'name': 'id', 'fields': []}, {'name': 'name', 'fields': []}, {'name': '_defaultColorIndex', 'fields': []}, {'name': 'summaryTable', 'args': [{'name': 'tableKey', 'value': 'timesteps_scatter_table'}], 'fields': []}]}]}}} None
+2025-12-15 18:49:27,641 INFO    wandb-AsyncioManager-main:1281414 [logging.py:_log():419] Reached EOF.
+2025-12-15 18:49:27,641 INFO    wandb-AsyncioManager-main:1281414 [logging.py:_log():419] Closing mailbox, abandoning 1 handles.