BMMBJEZ AI Image Generation

Discover BMMBJEZ for cutting-edge AI image generation. Generate stunning visuals from text, perform intricate inpainting, and transfer styles.

Platform: Replicate

Image GenerationInpaintingLoRA AdaptationText-to-Image

90 runs

H100

License Check Required

🚀Function Overview

Generates and edits images using diffusion processes, supporting text prompts, image inputs for inpainting/translation, and LoRA weights for custom style customization.

Key Features

Text-to-image generation from prompts
Image inpainting with mask support
Image-to-image style translation
LoRA adapter integration for custom styles
Adjustable resolution, quality, and sampling parameters
Fast quantized mode for quicker generation

Use Cases

•Creating artwork from textual descriptions
•Editing existing images via inpainting
•Applying artistic styles to photos
•Generating variations of portraits or concepts
•Rapid prototyping of visual designs

⚙️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": "bmmbjez in a professional CV headshot with confident posture, relaxed expression, anatomically correct, athletic build approximately 35kg less bodyweight with visibly slimmer face and refined jawline, natural silver hair in a tight quiff and darker upper-lip moustache exactly as in original LoRA training images (no lower-lip moustache), wearing a business-smart outfit with dark soft blazer and open-collar light shirt for a more relaxed professional look, oversized clear-framed rectangular Perspex glasses with wide transparent temples, identical in style to Jacquemus “Baci” model, cinematic studio portrait lighting with balanced soft key and fill lights to highlight facial structure, crisp focus with brilliant colours, tightly cropped to head and shoulders only, clean neutral but colourful background (e.g. soft blue or warm-toned gradient), no hands or fingers visible",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "png",
  "guidance_scale": 3.5,
  "output_quality": 80,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

Output Results

https://replicate.delivery/xezq/XjPnzW4SopILLBPSdPY1YSkSXvDQUpWTGlwe14lyztW6vyTKA/out-0.png

Quick Actions

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Technical Specifications

Hardware Type: H100
Run Count: 90
Commercial Use: Unknown/Restricted
Platform: Replicate

Related Keywords

Text-to-Image GenerationImage InpaintingImage-to-Image Style TransferLoRA AdaptationCreating ArtworkEditing ImagesApplying Artistic StylesRapid Prototyping

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