Instructions to use NoMoreCopyrightOrg/flux-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use NoMoreCopyrightOrg/flux-dev with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("NoMoreCopyrightOrg/flux-dev", 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
Upload handler.py
Browse files- handler.py +3 -3
handler.py
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@@ -17,9 +17,9 @@ def compile_pipeline(pipe) -> Any:
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pipe.transformer.fuse_qkv_projections()
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pipe.transformer.to(memory_format=torch.channels_last)
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pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead", fullgraph=False, dynamic=False, backend="inductor")
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pipe.vae.fuse_qkv_projections()
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pipe.vae.to(memory_format=torch.channels_last)
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pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=False, dynamic=False, backend="inductor")
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return pipe
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class EndpointHandler:
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pipe.transformer.fuse_qkv_projections()
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pipe.transformer.to(memory_format=torch.channels_last)
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pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead", fullgraph=False, dynamic=False, backend="inductor")
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#pipe.vae.fuse_qkv_projections()
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#pipe.vae.to(memory_format=torch.channels_last)
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#pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=False, dynamic=False, backend="inductor")
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return pipe
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class EndpointHandler:
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