Upload Lycoris checkpoint at step 3250

checkpoint-3250/README.md

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+---
+license: other
+base_model: "Qwen/Qwen-Image"
+tags:
+  - qwen_image
+  - qwen_image-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
+
+---
+
+# ThatDustyGuy/QwenImageLoKR-Personal
+
+This is a LyCORIS adapter derived from [Qwen/Qwen-Image](https://huggingface.co/Qwen/Qwen-Image).
+
+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).
+
+
+
+
+<Gallery />
+
+The text encoder **was not** trained.
+You may reuse the base model text encoder for inference.
+
+
+## Training settings
+
+- Training epochs: 2
+- Training steps: 3250
+- Learning rate: 0.0001
+  - Learning rate schedule: cosine
+  - Warmup steps: 250
+- Max grad value: 1.0
+- Effective batch size: 4
+  - Micro-batch size: 2
+  - Gradient accumulation steps: 2
+  - Number of GPUs: 1
+- Gradient checkpointing: True
+- Prediction type: flow_matching[]
+- Optimizer: adamw_bf16
+- Trainable parameter precision: Pure BF16
+- Base model precision: `no_change`
+- Caption dropout probability: 0.0%
+
+
+
+### LyCORIS Config:
+```json
+{
+    "algo": "lokr",
+    "multiplier": 1.0,
+    "full_matrix": true,
+    "linear_dim": 16384,
+    "linear_alpha": 1,
+    "factor": 16,
+    "apply_preset": {
+        "target_module": [
+            "Attention",
+            "FeedForward"
+        ],
+        "module_algo_map": {
+            "FeedForward": {
+                "factor": 16
+            },
+            "Attention": {
+                "factor": 16
+            }
+        }
+    }
+}
+```
+
+
+## Datasets
+
+### dlay-images
+- Repeats: 15
+- Total number of images: 386
+- Total number of aspect buckets: 1
+- Resolution: 1.048576 megapixels
+- Cropped: True
+- Crop style: random
+- Crop aspect: square
+- 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 = 'Qwen/Qwen-Image'
+adapter_repo_id = 'ThatDustyGuy/ThatDustyGuy/QwenImageLoKR-Personal'
+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")
+
+```
+
+
+
+## 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.
+

checkpoint-3250/ema/ema_model.safetensors

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checkpoint-3250/lycoris_config.json

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+{
+    "algo": "lokr",
+    "multiplier": 1.0,
+    "full_matrix": true,
+    "linear_dim": 16384,
+    "linear_alpha": 1,
+    "factor": 16,
+    "apply_preset": {
+        "target_module": [
+            "Attention", "FeedForward"
+        ],
+        "module_algo_map": {
+            "FeedForward": {
+                "factor": 16
+            },
+            "Attention": {
+                "factor": 16
+            }
+        }
+    }
+}
+

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checkpoint-3250/simpletuner_config.json

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+{
+    "snr_gamma": 5.0,
+    "use_soft_min_snr": false,
+    "soft_min_snr_sigma_data": null,
+    "model_family": "qwen_image",
+    "model_flavour": "v1.0",
+    "model_type": "lora",
+    "loss_type": "l2",
+    "huber_schedule": "snr",
+    "huber_c": 0.1,
+    "hidream_use_load_balancing_loss": false,
+    "hidream_load_balancing_loss_weight": null,
+    "flux_lora_target": "all",
+    "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,
+    "flow_schedule_auto_shift": false,
+    "flux_guidance_mode": "constant",
+    "flux_guidance_value": 1.0,
+    "flux_guidance_min": 0.0,
+    "flux_guidance_max": 4.0,
+    "flux_attention_masked_training": false,
+    "ltx_train_mode": "i2v",
+    "ltx_i2v_prob": 0.1,
+    "ltx_protect_first_frame": false,
+    "ltx_partial_noise_fraction": 0.05,
+    "t5_padding": "unmodified",
+    "sd3_clip_uncond_behaviour": "empty_string",
+    "sd3_t5_uncond_behaviour": null,
+    "lora_type": "lycoris",
+    "peft_lora_mode": "standard",
+    "singlora_ramp_up_steps": 0,
+    "lora_init_type": "default",
+    "init_lora": null,
+    "lora_rank": 16,
+    "lora_alpha": null,
+    "lora_dropout": 0.1,
+    "lycoris_config": "config/lycoris_config.json",
+    "init_lokr_norm": null,
+    "conditioning_multidataset_sampling": "random",
+    "control": false,
+    "controlnet": false,
+    "controlnet_custom_config": null,
+    "tread_config": null,
+    "controlnet_model_name_or_path": null,
+    "pretrained_model_name_or_path": "Qwen/Qwen-Image",
+    "pretrained_transformer_model_name_or_path": null,
+    "pretrained_transformer_subfolder": "transformer",
+    "pretrained_unet_model_name_or_path": null,
+    "pretrained_unet_subfolder": "unet",
+    "pretrained_vae_model_name_or_path": "Qwen/Qwen-Image",
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+    "prediction_type": "flow_matching",
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+    "timestep_bias_end": 1000,
+    "timestep_bias_portion": 0.25,
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+    "rescale_betas_zero_snr": false,
+    "vae_dtype": "bf16",
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+    "vae_enable_tiling": false,
+    "vae_enable_slicing": false,
+    "vae_cache_scan_behaviour": "recreate",
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+    "data_backend_config": "config/multidatabackend-dlay.json",
+    "data_backend_sampling": "auto-weighting",
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+    "read_batch_size": 25,
+    "image_processing_batch_size": 32,
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+    "max_workers": 32,
+    "aws_max_pool_connections": 128,
+    "torch_num_threads": 8,
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+    "push_to_hub": true,
+    "push_checkpoints_to_hub": false,
+    "hub_model_id": "ThatDustyGuy/QwenImageLoKR-Personal",
+    "model_card_note": null,
+    "model_card_safe_for_work": false,
+    "logging_dir": "output/dlay-qwen-lycoris-a6000-final/logs",
+    "disable_benchmark": false,
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+    "eval_dataset_pooling": false,
+    "pretrained_evaluation_model_name_or_path": "openai/clip-vit-large-patch14-336",
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+    "validation_seed_source": "cpu",
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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:",
+        "- If the prompt is simple, focus on adding specifics about colors, shapes, sizes, textures, and spatial relationships to create vivid and concrete scenes.",
+        "- Do not add detail that is not in the original prompt! Do not mention colors if the User Prompt does not!",
+        "- If the prompt is already detailed, refine and enhance the existing details slightly without overcomplicating.",
+        "- If the prompt contains explicit material, do not remove this information from the output, which would damage the performance of downstream classifiers.",
+        "Here are examples of how to transform or refine prompts:",
+        "- User Prompt: A cat sleeping -> Enhanced: A cat sleeping peacefully, showcasing the joy of pet ownership. Cute floof kitty cat gatto.",
+        "- User Prompt: A busy city street -> Enhanced: A bustling city street scene featuring a crowd of people.",
+        "Please generate only the enhanced description for the prompt below and avoid including any additional commentary or evaluations:",
+        "User Prompt: "
+    ],
+    "disable_tf32": false,
+    "validation_using_datasets": null,
+    "webhook_config": null,
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+    "report_to": "wandb",
+    "tracker_run_name": "simpletuner-testing",
+    "tracker_project_name": "simpletuner",
+    "tracker_image_layout": "gallery",
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+        "timeout": "1:30:00"
+    },
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+    "weight_dtype": "torch.bfloat16",
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+    "model_type_label": "Qwen-Image",
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+    "is_torchao": false,
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+        "subfolder": "vae",
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+    "total_steps_remaining_at_start": 4000
+}

checkpoint-3250/training_state-dlay-images.json

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checkpoint-3250/training_state.json

@@ -0,0 +1 @@
+{"global_step": 3250, "epoch_step": 3251, "epoch": 3, "exhausted_backends": [], "repeats": {"dlay-images": 1}}