Instructions to use lzq49/mvcontrol-4v-canny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lzq49/mvcontrol-4v-canny with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lzq49/mvcontrol-4v-canny", 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
Download diffusion_pytorch_model.bin from lzq49/mvcontrol-4v-canny: direct link, hf CLI and curl.
- Browser
- Download file 733 MB
-
https://huggingface.co/lzq49/mvcontrol-4v-canny/resolve/main/diffusion_pytorch_model.bin
- Command line
-
hf download hf://lzq49/mvcontrol-4v-canny/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/lzq49/mvcontrol-4v-canny/resolve/main/diffusion_pytorch_model.bin
733 MB
- Xet hash:
- cac1537f626437526959dcb7f5e19c17725bd6fe4ceb14b1cd80320fd665f82a
- Size of remote file:
- 733 MB
- SHA256:
- 122cabc4a6f32a57f6f8d131bbcfc173177ccfd370440838aac811de699d2e6c
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