Instructions to use Renz-7/RenderMatte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Renz-7/RenderMatte with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Renz-7/RenderMatte", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
RenderMatte
This repository contains RenderMatte matting model weights.
Checkpoints
base_sft10000/: SFT-trained base transformer checkpoint after 10,000 SFT steps.grpo_lora_step300/: LoRA weights obtained from GRPO training at step 300. This folder includes both the fused LoRA file and the PEFT adapter files.
Files
base_sft10000/
transformer/
config.json
diffusion_pytorch_model.safetensors
grpo_lora_step300/
e2p_fused_lora.safetensors
peft_lora/
adapter_config.json
adapter_model.safetensors
These weights are intended for research use with the corresponding RenderMatte inference code.
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