MrForExample
fa383bd37e
- Update readme to log the RCFG experiment - Refactor code - Add delta parameter on Animate_Anyone_Sampler node - Add ComfyUI Sampler progress bar
38 lines
1.2 KiB
Python
38 lines
1.2 KiB
Python
import time
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import torch
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from diffusers import (StableDiffusionPipeline,
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EulerAncestralDiscreteScheduler)
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from sfast.compilers.diffusion_pipeline_compiler import (compile,
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CompilationConfig)
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def load_model():
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model = StableDiffusionPipeline.from_pretrained(
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'runwayml/stable-diffusion-v1-5',
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torch_dtype=torch.float16)
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model.scheduler = EulerAncestralDiscreteScheduler.from_config(
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model.scheduler.config)
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model.safety_checker = None
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model.to(torch.device('cuda'))
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return model
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model = load_model()
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config = CompilationConfig.Default()
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# xformers and Triton are suggested for achieving best performance.
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try:
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import xformers
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config.enable_xformers = True
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except ImportError:
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print('xformers not installed, skip')
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try:
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import triton
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config.enable_triton = True
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except ImportError:
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print('Triton not installed, skip')
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# CUDA Graph is suggested for small batch sizes and small resolutions to reduce CPU overhead.
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# But it can increase the amount of GPU memory used.
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# For StableVideoDiffusionPipeline it is not needed.
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config.enable_cuda_graph = True
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model = compile(model, config) |