Mafuwoodstock Image Generation
Discover Mafuwoodstock, a versatile diffusion model for advanced image generation and editing. Discover how this AI model can transform your workflow!
🚀Function Overview
A diffusion-based model for generating and editing images using text prompts, supporting image-to-image transformations, inpainting, LoRA integration, and customizable resolution settings.
Key Features
- Text-to-image generation via prompts
- Image inpainting with masks
- Support for LoRA weight integration
- Customizable aspect ratio and resolution
- Multiple model variants for speed/quality trade-offs
- FP8 quantization for faster inference
- Safety checker toggle for generated content
Use Cases
- •Creating artwork from textual descriptions
- •Editing existing images by inpainting specific areas
- •Generating style-consistent images using LoRA adaptations
- •Producing multiple image variations quickly
⚙️Input Parameters
prompt
stringPrompt 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.
image
stringInput image for image to image or inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored.
mask
stringImage mask for image inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored.
aspect_ratio
stringAspect ratio for the generated image. If custom is selected, uses height and width below & will run in bf16 mode.
height
integerHeight of generated image. Only works if `aspect_ratio` is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation.
width
integerWidth 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_strength
numberPrompt strength when using img2img. 1.0 corresponds to full destruction of information in image.
model
stringWhich model to run inference with. The dev model performs best with around 28 inference steps but the schnell model only needs 4 steps.
num_outputs
integerNumber of outputs to generate.
num_inference_steps
integerNumber of denoising steps. More steps can give more detailed images, but take longer.
guidance_scale
numberGuidance scale for the diffusion process. Lower values can give more realistic images. Good values to try are 2, 2.5, 3 and 3.5.
seed
integerRandom seed. Set for reproducible generation.
output_format
stringFormat of the output images.
output_quality
integerQuality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs.
disable_safety_checker
booleanDisable safety checker for generated images.
go_fast
booleanRun faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16.
megapixels
stringApproximate number of megapixels for generated image.
lora_scale
numberDetermines 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.
extra_lora
stringLoad 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'.
extra_lora_scale
numberDetermines 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.
💡Usage Examples
Example 1
Input Parameters
{ "model": "dev", "prompt": "a wrestling fight of policemen in the surrounding of WoodStock_TOK infront of thousands of people", "go_fast": false, "lora_scale": 1.16, "megapixels": "1", "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }
Output Results
Quick Actions
Technical Specifications
- Hardware Type
- H100
- Run Count
- 9
- Commercial Use
- Unknown/Restricted
- Platform
- Replicate
Related Keywords
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