Model card auto-generated by SimpleTuner

README.md

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+---
+license: other
+base_model: "black-forest-labs/FLUX.1-dev"
+tags:
+  - flux
+  - flux-diffusers
+  - text-to-image
+  - diffusers
+  - simpletuner
+  - not-for-all-audiences
+  - lora
+  - template:sd-lora
+  - lycoris
+inference: true
+widget:
+- text: 'unconditional (blank prompt)'
+  parameters:
+    negative_prompt: 'blurry, cropped, ugly'
+  output:
+    url: ./assets/image_0_0.png
+- text: 'A photo-realistic image of a cat'
+  parameters:
+    negative_prompt: 'blurry, cropped, ugly'
+  output:
+    url: ./assets/image_1_0.png
+---
+
+# simpletuner-lora
+
+This is a LyCORIS adapter derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev).
+
+
+The main validation prompt used during training was:
+```
+A photo-realistic image of a cat
+```
+
+
+## Validation settings
+- CFG: `3.0`
+- CFG Rescale: `0.0`
+- Steps: `20`
+- Sampler: `FlowMatchEulerDiscreteScheduler`
+- Seed: `42`
+- Resolution: `1024x1024`
+- Skip-layer guidance: 
+
+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: 5
+- Training steps: 500
+- Learning rate: 0.0001
+  - Learning rate schedule: polynomial
+  - Warmup steps: 100
+- Max grad norm: 2.0
+- Effective batch size: 1
+  - Micro-batch size: 1
+  - Gradient accumulation steps: 1
+  - Number of GPUs: 1
+- Gradient checkpointing: True
+- Prediction type: flow-matching (extra parameters=['shift=3', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0', 'flow_matching_loss=compatible'])
+- Optimizer: adamw_bf16
+- Trainable parameter precision: Pure BF16
+- Caption dropout probability: 0.0%
+
+### LyCORIS Config:
+```json
+{
+    "algo": "lokr",
+    "multiplier": 1.0,
+    "linear_dim": 5000,
+    "linear_alpha": 1,
+    "factor": 16,
+    "apply_preset": {
+        "target_module": [
+            "Attention",
+            "FeedForward"
+        ],
+        "module_algo_map": {
+            "Attention": {
+                "factor": 16
+            },
+            "FeedForward": {
+                "factor": 8
+            }
+        }
+    }
+}
+```
+
+## Datasets
+
+### my-dataset-1024
+- Repeats: 10
+- Total number of images: 8
+- 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.1-dev'
+adapter_repo_id = 'Entiluke/simpletuner-lora'
+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 = "A photo-realistic image of a cat"
+
+
+## Optional: quantise the model to save on vram.
+## Note: The model was quantised during training, and so it is recommended to do the same 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
+image = pipeline(
+    prompt=prompt,
+    num_inference_steps=20,
+    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=3.0,
+).images[0]
+image.save("output.png", format="PNG")
+```
+