| --- |
| license: creativeml-openrail-m |
| library_name: diffusers |
| tags: |
| - stable-diffusion |
| - stable-diffusion-diffusers |
| - text-to-image |
| - diffusers |
| - controlnet |
| - diffusers-training |
| - stable-diffusion |
| - stable-diffusion-diffusers |
| - text-to-image |
| - diffusers |
| - controlnet |
| - diffusers-training |
| base_model: stabilityai/stable-diffusion-2-1-base |
| datasets: |
| - gvecchio/MatSynth |
| inference: true |
| --- |
| |
| # controlnet_normal |
| |
| Generate a normal map from a photograph or basecolor (albedo) map. |
| |
| # Usage |
| |
| ``` |
| import argparse |
| |
| from PIL import Image |
| from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler |
| from diffusers.utils import load_image |
| import torch |
|
|
| parser = argparse.ArgumentParser(description="Args for parser") |
| parser.add_argument("--seed", type=int, default=1, help="Seed for inference") |
| args = parser.parse_args() |
|
|
| base_model_path = "stabilityai/stable-diffusion-2-1-base" |
| controlnet_path = "sidnarsipur/controlnet_normal" |
|
|
| controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16) |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( |
| base_model_path, controlnet=controlnet, torch_dtype=torch.float16 |
| ) |
| |
| pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) |
| pipe.enable_xformers_memory_efficient_attention() |
| pipe.enable_model_cpu_offload() |
|
|
| control_image = load_image("inference/basecolor.png") #Change based on your image path |
| prompt = "Normal Map" #Don't change! |
|
|
| if control_image.size[0] > 2048 or control_image.size[1] > 2048: #Optional |
| control_image = control_image.resize((control_image.size[0] // 2, control_image.size[1] // 2)) |
| |
| generator = torch.manual_seed(args.seed) |
| |
| image = pipe( |
| prompt, num_inference_steps=50, generator=generator, image=control_image |
| ).images[0] |
| image.save("inference/normal.png") |
|
|
|
|
|
|
