gan-tu/flux-otake-cartoon 🖼️🔢❓📝✓ → 🖼️

▶️ 93 runs 📅 Jan 2025 ⚙️ Cog 0.11.1

About

A cartoon LORA trained on Otake from Takemoto Arash

Example Output

Prompt:

"A cartoon wolf OTAKE standing proudly in a vibrant Chinese New Year scene celebrating the Year of the Dragon. The wolf is dressed in a traditional red and gold Chinese jacket with intricate patterns, holding a glowing red lantern in one hand. Surrounding the wolf are festive decorations, including hanging red lanterns, gold ingots, and firecrackers. A majestic dragon dances in the background, its scales shimmering in shades of red and gold under the light of fireworks bursting in the night sky. The wolf’s orange fur and confident smile are fully visible, exuding a celebratory and festive vibe."

Output

Performance Metrics

28.21s Prediction Time

28.33s Total Time

All Input Parameters

{
  "model": "dev",
  "prompt": "A cartoon wolf OTAKE standing proudly in a vibrant Chinese New Year scene celebrating the Year of the Dragon. The wolf is dressed in a traditional red and gold Chinese jacket with intricate patterns, holding a glowing red lantern in one hand. Surrounding the wolf are festive decorations, including hanging red lanterns, gold ingots, and firecrackers. A majestic dragon dances in the background, its scales shimmering in shades of red and gold under the light of fireworks bursting in the night sky. The wolf’s orange fur and confident smile are fully visible, exuding a celebratory and festive vibe.",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 4,
  "aspect_ratio": "1:1",
  "output_format": "png",
  "guidance_scale": 3,
  "output_quality": 80,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

Input Parameters

mask Type: string: Image mask for image inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored.
seed Type: integer: Random seed. Set for reproducible generation
image Type: string: Input image for image to image or inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored.
model Default: dev: Which model to run inference with. The dev model performs best with around 28 inference steps but the schnell model only needs 4 steps.
width Type: integerRange: 256 - 1440: Width of generated image. Only works if `aspect_ratio` is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation
height Type: integerRange: 256 - 1440: Height of generated image. Only works if `aspect_ratio` is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation
prompt (required) Type: string: Prompt for generated image. If you include the `trigger_word` used in the training process you are more likely to activate the trained object, style, or concept in the resulting image.
go_fast Type: booleanDefault: false: Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16
extra_lora Type: string: Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars'
lora_scale Type: numberDefault: 1Range: -1 - 3: Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora.
megapixels Default: 1: Approximate number of megapixels for generated image
num_outputs Type: integerDefault: 1Range: 1 - 4: Number of outputs to generate
aspect_ratio Default: 1:1: Aspect ratio for the generated image. If custom is selected, uses height and width below & will run in bf16 mode
output_format Default: webp: Format of the output images
guidance_scale Type: numberDefault: 3Range: 0 - 10: Guidance scale for the diffusion process. Lower values can give more realistic images. Good values to try are 2, 2.5, 3 and 3.5
output_quality Type: integerDefault: 80Range: 0 - 100: Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs
prompt_strength Type: numberDefault: 0.8Range: 0 - 1: Prompt strength when using img2img. 1.0 corresponds to full destruction of information in image
extra_lora_scale Type: numberDefault: 1Range: -1 - 3: Determines how strongly the extra LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora.
replicate_weights Type: string: Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars'
num_inference_steps Type: integerDefault: 28Range: 1 - 50: Number of denoising steps. More steps can give more detailed images, but take longer.
disable_safety_checker Type: booleanDefault: false: Disable safety checker for generated images.

Output Schema

Output

Type: array • Items Type: string • Items Format: uri

Example Execution Logs

2025-01-05 15:21:02.612 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys
2025-01-05 15:21:02.612 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted
Applying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]
Applying LoRA:  89%|████████▉ | 271/304 [00:00<00:00, 2702.17it/s]
Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2596.05it/s]
2025-01-05 15:21:02.730 | SUCCESS  | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.12s
free=29538762596352
Downloading weights
2025-01-05T15:21:02Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpm8r2dwtk/weights url=https://replicate.delivery/xezq/2IDjyk6OU8o6Ed2IoN0jtV6GacLodB68J7HPHWCEtsG5sdAF/trained_model.tar
2025-01-05T15:21:05Z | INFO  | [ Complete ] dest=/tmp/tmpm8r2dwtk/weights size="172 MB" total_elapsed=3.063s url=https://replicate.delivery/xezq/2IDjyk6OU8o6Ed2IoN0jtV6GacLodB68J7HPHWCEtsG5sdAF/trained_model.tar
Downloaded weights in 3.09s
2025-01-05 15:21:05.818 | INFO     | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/66ba6de0f4aaf80c
2025-01-05 15:21:05.890 | INFO     | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded
2025-01-05 15:21:05.890 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys
2025-01-05 15:21:05.890 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted
Applying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]
Applying LoRA:  89%|████████▉ | 272/304 [00:00<00:00, 2716.22it/s]
Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2603.00it/s]
2025-01-05 15:21:06.007 | SUCCESS  | fp8.lora_loading:load_lora:539 - LoRA applied in 0.19s
Using seed: 20308
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Total safe images: 4 out of 4

Version Details

Version ID: 088e3fb59c4814c0928d359e30ad212887be25ed6cf47050bdab3d9581d023b0
Version Created: January 4, 2025

Run on Replicate →