Instructions to use Disty0/FLUX.2-dev-SDNQ-uint4-svd-r32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Disty0/FLUX.2-dev-SDNQ-uint4-svd-r32 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Disty0/FLUX.2-dev-SDNQ-uint4-svd-r32", 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
- Local Apps
- Draw Things
- DiffusionBee
Update README.md
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README.md
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@@ -32,7 +32,7 @@ pipe = diffusers.Flux2Pipeline.from_pretrained("Disty0/FLUX.2-dev-SDNQ-uint4-svd
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if triton_is_available and (torch.cuda.is_available() or torch.xpu.is_available()):
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pipe.transformer = apply_sdnq_options_to_model(pipe.transformer, use_quantized_matmul=True)
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pipe.text_encoder = apply_sdnq_options_to_model(pipe.text_encoder, use_quantized_matmul=True)
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pipe.transformer = torch.compile(pipe.transformer) # optional for faster speeds
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pipe.enable_model_cpu_offload()
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if triton_is_available and (torch.cuda.is_available() or torch.xpu.is_available()):
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pipe.transformer = apply_sdnq_options_to_model(pipe.transformer, use_quantized_matmul=True)
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pipe.text_encoder = apply_sdnq_options_to_model(pipe.text_encoder, use_quantized_matmul=True)
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# pipe.transformer = torch.compile(pipe.transformer) # optional for faster speeds
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pipe.enable_model_cpu_offload()
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