Buckets:
| import torch | |
| from controlnet_aux import CannyDetector | |
| from diffusers import FluxControlPipeline | |
| from diffusers.utils import load_image | |
| from nunchaku import NunchakuFluxTransformer2dModel | |
| from nunchaku.utils import get_precision | |
| precision = get_precision() # auto-detect your precision is 'int4' or 'fp4' based on your GPU | |
| transformer = NunchakuFluxTransformer2dModel.from_pretrained( | |
| f"nunchaku-tech/nunchaku-flux.1-canny-dev/svdq-{precision}_r32-flux.1-canny-dev.safetensors" | |
| ) | |
| pipe = FluxControlPipeline.from_pretrained( | |
| "black-forest-labs/FLUX.1-Canny-dev", transformer=transformer, torch_dtype=torch.bfloat16 | |
| ).to("cuda") | |
| prompt = ( | |
| "A robot made of exotic candies and chocolates of different kinds. " | |
| "The background is filled with confetti and celebratory gifts." | |
| ) | |
| control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png") | |
| processor = CannyDetector() | |
| control_image = processor( | |
| control_image, low_threshold=50, high_threshold=200, detect_resolution=1024, image_resolution=1024 | |
| ) | |
| image = pipe( | |
| prompt=prompt, control_image=control_image, height=1024, width=1024, num_inference_steps=50, guidance_scale=30.0 | |
| ).images[0] | |
| image.save(f"flux.1-canny-dev-{precision}.png") | |
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- 1.26 kB
- Xet hash:
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