--- 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: 'In this scene from the animated series "Helluva Boss," Loona, the wolf-like receptionist of the Immediate Murder Professionals (I.M.P), is depicted leaning against a wall outside the office. She is casually engrossed in her phone, displaying her typical aloof and detached demeanor. Loona''s appearance includes her usual whitish fur, light grey hair, black-tipped ears, and red eyes, complemented by her punk-inspired attire featuring a black choker with spikes, a dark grey top, fingerless wrist-length black gloves, and black shorts.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_1_0.png - text: 'Loona shrugs with an exasperated expression, her red eyes wide and frustrated, as she seemingly questions or challenges something said in the I.M.P office. Still from Helluva boss. Loona''s appearance includes her usual whitish fur, light grey hair, black-tipped ears, and red eyes, complemented by her punk-inspired attire featuring a black choker with spikes, a dark grey top, fingerless wrist-length black gloves, and black shorts.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_2_0.png - text: 'A scene from the animated series "Helluva Boss," set in the office. Loona, the wolf-like receptionist with white fur, black-tipped ears, and red eyes, is seated on a couch, facing towards the viewer. Loona''s appearance is complemented by her punk-inspired attire featuring a black choker with spikes, a dark grey top, fingerless wrist-length black gloves, and black shorts. She holds a piece of paper that says,"Welcome to Losercity, jerks". In the background, the office has a striped wall pattern and visible damage on the ceiling, indicating a chaotic or rough environment. On the right side of the image, two imp characters appear to be engaged in conversation.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_3_0.png - text: 'Loona from Helluva Boss is dressed in an oversized taco costume, looking visibly irritated and embarrassed. Her red eyes convey her annoyance as she crosses her arms and glares to the side. Loona''s appearance includes her usual whitish fur, light grey hair, black-tipped ears, and red eyes' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_4_0.png - text: 'Loona is standing next to Blitzo (Helluva boss)' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_5_0.png - text: 'In this "Helluva Boss" scene, Loona, the wolf-like receptionist, stands in an elevator with a tense and irritated expression, her teeth bared in a snarl. Blitzø, the red demon with distinctive black and white horns, leans close and makes an adorable look, as if asking for a favor. The ornate elevator setting hints at a tense moment, possibly involving a challenging mission or conflict within the I.M.P team.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_6_0.png - text: 'a 2D simple drawing of a madeleine cake, with a green cloud drawn next to it' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_7_0.png - text: 'a 3D captivating YouTube thumbnail depicting of a full detailed,it''s on a party real people like, on front there is a giant pulling a nose of a black African real like lady down to size of elephant nose,be creative and unique' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_8_0.png - text: 'Whiskers the cat. Whiskers becomes a mentor to other animals.Impressed by Whiskers'' intelligence, other animals in the neighborhood seek his guidance. Whiskers sets up a virtual learning platform using AI technology, where animals can ask questions, receive personalized lessons, and acquire knowledge in various subjects. Whiskers becomes a mentor, helping others unlock their potential.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_9_0.png - text: 'As the stock market fluctuates, the investor remains calm and collected at their desk, surrounded by charts and graphs. Their tailored suit and polished briefcase are a symbol of their expertise and experience in the world of finance. ' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_10_0.png - text: 'loona from helluva boss is eating a donut' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_11_0.png --- # flux-training-losercity-next-lycoris12 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: ``` loona from helluva boss is eating a donut ``` ## Validation settings - CFG: `3.5` - CFG Rescale: `0.0` - Steps: `15` - Sampler: `None` - Seed: `42` - Resolution: `1024` 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: The text encoder **was not** trained. You may reuse the base model text encoder for inference. ## Training settings - Training epochs: 0 - Training steps: 2200 - Learning rate: 4e-05 - Effective batch size: 16 - Micro-batch size: 1 - Gradient accumulation steps: 16 - Number of GPUs: 1 - Prediction type: flow-matching - Rescaled betas zero SNR: False - Optimizer: adamw_bf16 - Precision: Pure BF16 - Quantised: Yes: fp8-quanto - Xformers: Not used - LyCORIS Config: ```json { "algo": "lokr", "multiplier": 1.0, "linear_dim": 1000000, "linear_alpha": 1, "factor": 10, "full_matrix": true, "apply_preset": { "target_module": [ "FluxTransformerBlock", "FluxSingleTransformerBlock" ], "name_algo_map": { "transformer_blocks.[0-7]*": { "algo": "lokr", "factor": 4, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.[8-15]*": { "algo": "lokr", "factor": 6, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "transformer_blocks.[16-18]*": { "algo": "lokr", "factor": 12, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "single_transformer_blocks.[0-15]*": { "algo": "lokr", "factor": 8, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "single_transformer_blocks.[16-23]*": { "algo": "lokr", "factor": 6, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true }, "single_transformer_blocks.[24-37]*": { "algo": "lokr", "factor": 4, "linear_dim": 1000000, "linear_alpha": 1, "full_matrix": true } }, "use_fnmatch": true } } ``` ## Datasets ### default_dataset_arb - Repeats: 9999 - Total number of images: 41 - Total number of aspect buckets: 1 - Resolution: 1.33 megapixels - Cropped: False - Crop style: None - Crop aspect: None ### default_dataset_arb2 - Repeats: 9999 - Total number of images: 2565 - Total number of aspect buckets: 1 - Resolution: 1.33 megapixels - Cropped: False - Crop style: None - Crop aspect: None ### default_dataset_arb3 - Repeats: 9999 - Total number of images: 3220 - Total number of aspect buckets: 1 - Resolution: 1.33 megapixels - Cropped: False - Crop style: None - Crop aspect: None ### default_dataset - Repeats: 9999 - Total number of images: 42 - Total number of aspect buckets: 1 - Resolution: 1.048576 megapixels - Cropped: True - Crop style: center - Crop aspect: square ### default_dataset_512 - Repeats: 9999 - Total number of images: 42 - Total number of aspect buckets: 1 - Resolution: 0.262144 megapixels - Cropped: True - Crop style: center - Crop aspect: square ### default_dataset_640 - Repeats: 9999 - Total number of images: 42 - Total number of aspect buckets: 1 - Resolution: 0.4096 megapixels - Cropped: True - Crop style: center - Crop aspect: square ### default_dataset_768 - Repeats: 9999 - Total number of images: 42 - Total number of aspect buckets: 1 - Resolution: 0.589824 megapixels - Cropped: True - Crop style: center - Crop aspect: square ### default_dataset_896 - Repeats: 9999 - Total number of images: 42 - Total number of aspect buckets: 1 - Resolution: 0.802816 megapixels - Cropped: True - Crop style: center - Crop aspect: square ### default_dataset_uncaptioned - Repeats: 9999 - Total number of images: 2565 - Total number of aspect buckets: 1 - Resolution: 1.048576 megapixels - Cropped: True - Crop style: center - Crop aspect: square ### default_dataset_uncaptioned_512 - Repeats: 9999 - Total number of images: 2565 - Total number of aspect buckets: 1 - Resolution: 0.262144 megapixels - Cropped: True - Crop style: center - Crop aspect: square ### default_dataset_art - Repeats: 9999 - Total number of images: 2482 - Total number of aspect buckets: 1 - Resolution: 1.048576 megapixels - Cropped: True - Crop style: center - Crop aspect: square ### default_dataset_art_512 - Repeats: 9999 - Total number of images: 3193 - Total number of aspect buckets: 1 - Resolution: 0.262144 megapixels - Cropped: True - Crop style: center - Crop aspect: square ### default_dataset_art_640 - Repeats: 9999 - Total number of images: 3115 - Total number of aspect buckets: 1 - Resolution: 0.4096 megapixels - Cropped: True - Crop style: random - Crop aspect: square ### default_dataset_art_768 - Repeats: 9999 - Total number of images: 2989 - Total number of aspect buckets: 1 - Resolution: 0.589824 megapixels - Cropped: True - Crop style: random - Crop aspect: square ### default_dataset_art_896 - Repeats: 9999 - Total number of images: 2787 - Total number of aspect buckets: 1 - Resolution: 0.802816 megapixels - Cropped: True - Crop style: random - Crop aspect: square ## Inference ```python import torch from diffusers import DiffusionPipeline from lycoris import create_lycoris_from_weights model_id = 'black-forest-labs/FLUX.1-dev' adapter_id = 'pytorch_lora_weights.safetensors' # you will have to download this manually lora_scale = 1.0 wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer) wrapper.merge_to() prompt = "loona from helluva boss is eating a donut" pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') image = pipeline( prompt=prompt, num_inference_steps=15, generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826), width=1024, height=1024, guidance_scale=3.5, ).images[0] image.save("output.png", format="PNG") ```