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109 lines
2.9 KiB
109 lines
2.9 KiB
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CPU = 0 |
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NO_VRAM = 1 |
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LOW_VRAM = 2 |
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NORMAL_VRAM = 3 |
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accelerate_enabled = False |
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vram_state = NORMAL_VRAM |
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total_vram = 0 |
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total_vram_available_mb = -1 |
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import sys |
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set_vram_to = NORMAL_VRAM |
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if "--lowvram" in sys.argv: |
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set_vram_to = LOW_VRAM |
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if "--novram" in sys.argv: |
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set_vram_to = NO_VRAM |
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try: |
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import torch |
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total_vram = torch.cuda.mem_get_info(torch.cuda.current_device())[1] / (1024 * 1024) |
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except: |
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pass |
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if set_vram_to != NORMAL_VRAM: |
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try: |
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import accelerate |
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accelerate_enabled = True |
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vram_state = set_vram_to |
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except Exception as e: |
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import traceback |
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print(traceback.format_exc()) |
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print("ERROR: COULD NOT ENABLE LOW VRAM MODE.") |
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total_vram_available_mb = (total_vram - 1024) // 2 |
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total_vram_available_mb = int(max(256, total_vram_available_mb)) |
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print("Set vram state to:", ["CPU", "NO VRAM", "LOW VRAM", "NORMAL VRAM"][vram_state]) |
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current_loaded_model = None |
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model_accelerated = False |
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def unload_model(): |
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global current_loaded_model |
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global model_accelerated |
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if current_loaded_model is not None: |
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if model_accelerated: |
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accelerate.hooks.remove_hook_from_submodules(current_loaded_model.model) |
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model_accelerated = False |
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current_loaded_model.model.cpu() |
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current_loaded_model.unpatch_model() |
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current_loaded_model = None |
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def load_model_gpu(model): |
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global current_loaded_model |
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global vram_state |
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global model_accelerated |
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if model is current_loaded_model: |
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return |
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unload_model() |
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try: |
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real_model = model.patch_model() |
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except Exception as e: |
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model.unpatch_model() |
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raise e |
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current_loaded_model = model |
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if vram_state == CPU: |
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pass |
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elif vram_state == NORMAL_VRAM: |
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model_accelerated = False |
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real_model.cuda() |
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else: |
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if vram_state == NO_VRAM: |
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device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "256MiB", "cpu": "16GiB"}) |
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elif vram_state == LOW_VRAM: |
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device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "{}MiB".format(total_vram_available_mb), "cpu": "16GiB"}) |
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accelerate.dispatch_model(real_model, device_map=device_map, main_device="cuda") |
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model_accelerated = True |
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return current_loaded_model |
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def get_free_memory(): |
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dev = torch.cuda.current_device() |
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stats = torch.cuda.memory_stats(dev) |
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mem_active = stats['active_bytes.all.current'] |
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mem_reserved = stats['reserved_bytes.all.current'] |
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mem_free_cuda, _ = torch.cuda.mem_get_info(dev) |
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mem_free_torch = mem_reserved - mem_active |
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return mem_free_cuda + mem_free_torch |
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def maximum_batch_area(): |
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global vram_state |
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if vram_state == NO_VRAM: |
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return 0 |
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memory_free = get_free_memory() / (1024 * 1024) |
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area = ((memory_free - 1024) * 0.9) / (0.6) |
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return int(max(area, 0))
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