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--- |
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license: other |
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base_model: "black-forest-labs/FLUX.1-dev" |
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tags: |
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- flux |
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- flux-diffusers |
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- text-to-image |
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- diffusers |
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- simpletuner |
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- not-for-all-audiences |
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- lora |
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- template:sd-lora |
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- lycoris |
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inference: true |
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widget: |
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- text: 'unconditional (blank prompt)' |
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parameters: |
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negative_prompt: 'blurry, cropped, ugly' |
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output: |
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url: ./assets/image_0_0.png |
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- text: 'In the style of a c4ss4tt oil painting, A child wearing an elaborate blue silk dress with ruffles and white lace trim sits near a window, the fabric catching soft light.' |
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parameters: |
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negative_prompt: 'blurry, cropped, ugly' |
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output: |
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url: ./assets/image_1_0.png |
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- text: 'In the style of a c4ss4tt oil painting, A close portrait of a young child''s face with rosy cheeks and delicate features, gentle lighting from a nearby window.' |
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parameters: |
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negative_prompt: 'blurry, cropped, ugly' |
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output: |
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url: ./assets/image_2_0.png |
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- text: 'In the style of a c4ss4tt oil painting, Strong window light falls across a child''s face and shoulder, creating bold shadows on their blue dress.' |
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parameters: |
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negative_prompt: 'blurry, cropped, ugly' |
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output: |
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url: ./assets/image_3_0.png |
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- text: 'In the style of a c4ss4tt oil painting, A child in a blue hat stands by a window.' |
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parameters: |
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negative_prompt: 'blurry, cropped, ugly' |
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output: |
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url: ./assets/image_4_0.png |
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- text: 'In the style of a c4ss4tt oil painting, A woman in soft colors holds her baby close.' |
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parameters: |
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negative_prompt: 'blurry, cropped, ugly' |
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output: |
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url: ./assets/image_5_0.png |
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- text: 'In the style of a c4ss4tt oil painting, A woman in a detailed white lace dress reads while seated by a window with gauzy curtains, various textures visible in the furnishings.' |
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parameters: |
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negative_prompt: 'blurry, cropped, ugly' |
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output: |
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url: ./assets/image_6_0.png |
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- text: 'In the style of a c4ss4tt oil painting, A mother in a textured knit sweater checks her phone while her baby sleeps against her shoulder.' |
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parameters: |
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negative_prompt: 'blurry, cropped, ugly' |
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output: |
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url: ./assets/image_7_0.png |
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- text: 'In the style of a c4ss4tt oil painting, A mother cat grooms her kitten by a sunlit window, their fur catching the light.' |
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parameters: |
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negative_prompt: 'blurry, cropped, ugly' |
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output: |
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url: ./assets/image_8_0.png |
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--- |
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# Mary-Cassatt-Oil-DetailCrops-Slow-Resume-NoTimeStepBias-7e-5-Flux-LoKr |
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This is a LyCORIS adapter derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev). |
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No validation prompt was used during training. |
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None |
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## Validation settings |
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- CFG: `3.0` |
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- CFG Rescale: `0.0` |
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- Steps: `20` |
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- Sampler: `None` |
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- Seed: `42` |
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- Resolution: `1024x1024` |
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Note: The validation settings are not necessarily the same as the [training settings](#training-settings). |
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You can find some example images in the following gallery: |
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<Gallery /> |
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The text encoder **was not** trained. |
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You may reuse the base model text encoder for inference. |
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## Training settings |
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- Training epochs: 3 |
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- Training steps: 2250 |
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- Learning rate: 7e-05 |
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- Max grad norm: 0.1 |
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- Effective batch size: 3 |
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- Micro-batch size: 3 |
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- Gradient accumulation steps: 1 |
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- Number of GPUs: 1 |
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- Prediction type: flow-matching (extra parameters=['shift=2.0', 'flux_guidance_value=1.0']) |
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- Rescaled betas zero SNR: False |
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- Optimizer: adamw_bf16 |
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- Precision: Pure BF16 |
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- Quantised: Yes: int8-quanto |
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- Xformers: Not used |
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- LyCORIS Config: |
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```json |
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{ |
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"algo": "lokr", |
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"multiplier": 1.0, |
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"linear_dim": 10000, |
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"linear_alpha": 1, |
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"factor": 16, |
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"apply_preset": { |
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"target_module": [ |
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"Attention", |
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"FeedForward" |
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], |
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"module_algo_map": { |
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"Attention": { |
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"factor": 16 |
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}, |
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"FeedForward": { |
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"factor": 8 |
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} |
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} |
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} |
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} |
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``` |
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## Datasets |
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### cassatt-detail-crops-512 |
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- Repeats: 15 |
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- Total number of images: 25 |
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- Total number of aspect buckets: 10 |
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- Resolution: 0.262144 megapixels |
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- Cropped: False |
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- Crop style: None |
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- Crop aspect: None |
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- Used for regularisation data: No |
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### cassatt-detail-crops-768 |
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- Repeats: 15 |
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- Total number of images: 25 |
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- Total number of aspect buckets: 10 |
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- Resolution: 0.589824 megapixels |
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- Cropped: False |
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- Crop style: None |
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- Crop aspect: None |
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- Used for regularisation data: No |
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### cassatt-detail-crops-1024 |
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- Repeats: 7 |
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- Total number of images: 25 |
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- Total number of aspect buckets: 17 |
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- Resolution: 1.048576 megapixels |
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- Cropped: False |
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- Crop style: None |
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- Crop aspect: None |
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- Used for regularisation data: No |
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### cassatt-detail-crops-1536 |
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- Repeats: 3 |
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- Total number of images: 24 |
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- Total number of aspect buckets: 17 |
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- Resolution: 2.359296 megapixels |
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- Cropped: False |
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- Crop style: None |
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- Crop aspect: None |
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- Used for regularisation data: No |
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## Inference |
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```python |
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import torch |
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from diffusers import DiffusionPipeline |
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from lycoris import create_lycoris_from_weights |
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def download_adapter(repo_id: str): |
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import os |
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from huggingface_hub import hf_hub_download |
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adapter_filename = "pytorch_lora_weights.safetensors" |
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cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models')) |
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cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_") |
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path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path) |
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path_to_adapter_file = os.path.join(path_to_adapter, adapter_filename) |
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os.makedirs(path_to_adapter, exist_ok=True) |
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hf_hub_download( |
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repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter |
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) |
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return path_to_adapter_file |
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model_id = 'black-forest-labs/FLUX.1-dev' |
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adapter_repo_id = 'davidrd123/Mary-Cassatt-Oil-DetailCrops-Slow-Resume-NoTimeStepBias-7e-5-Flux-LoKr' |
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adapter_filename = 'pytorch_lora_weights.safetensors' |
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adapter_file_path = download_adapter(repo_id=adapter_repo_id) |
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pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16 |
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lora_scale = 1.0 |
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wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer) |
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wrapper.merge_to() |
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prompt = "An astronaut is riding a horse through the jungles of Thailand." |
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## Optional: quantise the model to save on vram. |
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## Note: The model was quantised during training, and so it is recommended to do the same during inference time. |
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from optimum.quanto import quantize, freeze, qint8 |
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quantize(pipeline.transformer, weights=qint8) |
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freeze(pipeline.transformer) |
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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 |
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image = pipeline( |
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prompt=prompt, |
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num_inference_steps=20, |
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generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826), |
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width=1024, |
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height=1024, |
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guidance_scale=3.0, |
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).images[0] |
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image.save("output.png", format="PNG") |
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``` |
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