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https://github.com/easydiffusion/easydiffusion.git
synced 2025-01-23 14:50:54 +01:00
commit
bdb6649722
@ -22,20 +22,20 @@ mem_free_threshold = 0
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def get_device_delta(render_devices, active_devices):
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"""
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render_devices: 'cpu', or 'auto' or ['cuda:N'...]
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active_devices: ['cpu', 'cuda:N'...]
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render_devices: 'cpu', or 'auto', or 'mps' or ['cuda:N'...]
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active_devices: ['cpu', 'mps', 'cuda:N'...]
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"""
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if render_devices in ("cpu", "auto"):
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if render_devices in ("cpu", "auto", "mps"):
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render_devices = [render_devices]
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elif render_devices is not None:
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if isinstance(render_devices, str):
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render_devices = [render_devices]
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if isinstance(render_devices, list) and len(render_devices) > 0:
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render_devices = list(filter(lambda x: x.startswith("cuda:"), render_devices))
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render_devices = list(filter(lambda x: x.startswith("cuda:") or x == "mps", render_devices))
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if len(render_devices) == 0:
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raise Exception(
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'Invalid render_devices value in config.json. Valid: {"render_devices": ["cuda:0", "cuda:1"...]}, or {"render_devices": "cpu"} or {"render_devices": "auto"}'
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'Invalid render_devices value in config.json. Valid: {"render_devices": ["cuda:0", "cuda:1"...]}, or {"render_devices": "cpu"} or {"render_devices": "mps"} or {"render_devices": "auto"}'
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)
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render_devices = list(filter(lambda x: is_device_compatible(x), render_devices))
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@ -64,13 +64,26 @@ def get_device_delta(render_devices, active_devices):
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return devices_to_start, devices_to_stop
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def is_mps_available():
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return (
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platform.system() == "Darwin"
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and hasattr(torch.backends, "mps")
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and torch.backends.mps.is_available()
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and torch.backends.mps.is_built()
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)
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def is_cuda_available():
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return torch.cuda.is_available()
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def auto_pick_devices(currently_active_devices):
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global mem_free_threshold
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if platform.system() == "Darwin" and torch.backends.mps.is_available() and torch.backends.mps.is_built():
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if is_mps_available():
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return ["mps"]
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if not torch.cuda.is_available():
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if not is_cuda_available():
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return ["cpu"]
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device_count = torch.cuda.device_count()
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@ -160,14 +173,16 @@ def needs_to_force_full_precision(context):
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def get_max_vram_usage_level(device):
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if device != "cpu":
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if "cuda" in device:
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_, mem_total = torch.cuda.mem_get_info(device)
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mem_total /= float(10**9)
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else:
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return "high"
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if mem_total < 4.5:
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return "low"
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elif mem_total < 6.5:
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return "balanced"
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mem_total /= float(10**9)
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if mem_total < 4.5:
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return "low"
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elif mem_total < 6.5:
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return "balanced"
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return "high"
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@ -200,7 +215,7 @@ def is_device_compatible(device):
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log.error(str(e))
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return False
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if device == "cpu":
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if device in ("cpu", "mps"):
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return True
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# Memory check
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try:
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@ -385,7 +385,7 @@ def get_devices():
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}
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def get_device_info(device):
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if "cuda" not in device:
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if device in ("cpu", "mps"):
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return {"name": device_manager.get_processor_name()}
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mem_free, mem_total = torch.cuda.mem_get_info(device)
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@ -400,14 +400,17 @@ def get_devices():
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}
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# list the compatible devices
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gpu_count = torch.cuda.device_count()
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for device in range(gpu_count):
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cuda_count = torch.cuda.device_count()
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for device in range(cuda_count):
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device = f"cuda:{device}"
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if not device_manager.is_device_compatible(device):
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continue
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devices["all"].update({device: get_device_info(device)})
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if device_manager.is_mps_available():
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devices["all"].update({"mps": get_device_info("mps")})
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devices["all"].update({"cpu": get_device_info("cpu")})
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# list the activated devices
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