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Mafuwoodstock Image Generation

Discover Mafuwoodstock, a versatile diffusion model for advanced image generation and editing. Discover how this AI model can transform your workflow!

Platform: Replicate
Text-to-Image GenerationImage InpaintingLoRA ScalingDiffusion Models
9 runs
H100
License Check Required

🚀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

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.

image

string

Input image for image to image or inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored.

mask

string

Image mask for image inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored.

aspect_ratio

string

Aspect ratio for the generated image. If custom is selected, uses height and width below & will run in bf16 mode.

height

integer

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.

width

integer

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.

prompt_strength

number

Prompt strength when using img2img. 1.0 corresponds to full destruction of information in image.

model

string

Which 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

integer

Number of outputs to generate.

num_inference_steps

integer

Number of denoising steps. More steps can give more detailed images, but take longer.

guidance_scale

number

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.

seed

integer

Random seed. Set for reproducible generation.

output_format

string

Format of the output images.

output_quality

integer

Quality 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

boolean

Disable safety checker for generated images.

go_fast

boolean

Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16.

megapixels

string

Approximate number of megapixels for generated image.

lora_scale

number

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.

extra_lora

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'.

extra_lora_scale

number

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.

💡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

https://replicate.delivery/xezq/o4eguwLLoa1BTKwRFW77lbqTtwOjQHtuOPaywmYn4ovvfonUA/out-0.webp
https://replicate.delivery/xezq/xuQ3qManV4oHAFcxlf8sgwaetPORsULrMcG46HQRCVTfejekC/out-1.webp
https://replicate.delivery/xezq/Og2vMEUo11YYNFjCQuC6M2U1DCVhkc3GnQKEzl1t7h43P6JF/out-2.webp
https://replicate.delivery/xezq/wF6yHMgfUqU1Ea2yxzZFkfhkcD2GaxSkpfwfiyRHSkE99jekC/out-3.webp