Volcano3D Grey

Unleash your creativity with Volcano3D Grey, a powerful model for generating captivating 3D-style images. See what makes this AI model special!

Platform: Replicate

3D Image GenerationImage InpaintingLoRA IntegrationDiffusion Models

36 runs

H100

License Check Required

🚀Function Overview

Generates 3D-style images using text prompts and optional input images, supporting inpainting, style control via LoRA adaptations, and quality adjustments.

Key Features

Text-to-image generation with diffusion models
Image-to-image transformations using input references
Mask-based inpainting for selective edits
Custom aspect ratio and resolution control
Go Fast mode for accelerated inference
LoRA scale adjustments for style intensity
Safety checker toggling
Multiple model options (dev/schnell) for speed-quality tradeoff

Use Cases

•Creating concept art for 3D designs
•Product visualization (e.g., interior design tiles)
•Image editing via inpainting masks
•Style transfer using LoRA adaptations

⚙️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

{
  "image": "https://replicate.delivery/pbxt/MmzbdbOejuGJGm9HAb3gkEVLg3Gf6Zl7bxfnFEAEoh2PZuAA/living_sample1.jpg",
  "model": "dev",
  "prompt": "The modern living room is Volcano3D wall, VOLCANO3DGREY, .  Stone effect multi-level porcelain wall tile",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 1,
  "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/NCzo9FylxOJHDJszEBWffG1jfNe4GzHrWdXZwcTSoovgGEASB/out-0.webp

Quick Actions

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Technical Specifications

Hardware Type: H100
Run Count: 36
Commercial Use: Unknown/Restricted
Platform: Replicate

Related Keywords

3D Image GenerationImage InpaintingLoRA IntegrationText-to-image generationImage-to-image transformationsConcept art creationProduct visualizationStyle transfer

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