diff --git a/.github/workflows/windows_release_nightly_pytorch.yml b/.github/workflows/windows_release_nightly_pytorch.yml index c7ef93ce1..319942e7c 100644 --- a/.github/workflows/windows_release_nightly_pytorch.yml +++ b/.github/workflows/windows_release_nightly_pytorch.yml @@ -31,7 +31,7 @@ jobs: echo 'import site' >> ./python311._pth curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py ./python.exe get-pip.py - python -m pip wheel torch torchvision torchaudio aiohttp==3.8.4 --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu121 -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir + python -m pip wheel torch torchvision torchaudio aiohttp==3.8.5 --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu121 -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir ls ../temp_wheel_dir ./python.exe -s -m pip install --pre ../temp_wheel_dir/* sed -i '1i../ComfyUI' ./python311._pth diff --git a/README.md b/README.md index b055325ed..baa8cf8b6 100644 --- a/README.md +++ b/README.md @@ -47,6 +47,7 @@ Workflow examples can be found on the [Examples page](https://comfyanonymous.git | Ctrl + O | Load workflow | | Ctrl + A | Select all nodes | | Ctrl + M | Mute/unmute selected nodes | +| Ctrl + B | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through) | | Delete/Backspace | Delete selected nodes | | Ctrl + Delete/Backspace | Delete the current graph | | Space | Move the canvas around when held and moving the cursor | diff --git a/comfy/cli_args.py b/comfy/cli_args.py index ec7d34a55..374dd2f7d 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -82,6 +82,9 @@ vram_group.add_argument("--novram", action="store_true", help="When lowvram isn' vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).") +parser.add_argument("--disable-smart-memory", action="store_true", help="Force ComfyUI to agressively offload to regular ram instead of keeping models in vram when it can.") + + parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.") parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.") parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).") diff --git a/comfy/clip_vision.py b/comfy/clip_vision.py index 8d04faf71..2c8603bbe 100644 --- a/comfy/clip_vision.py +++ b/comfy/clip_vision.py @@ -25,6 +25,7 @@ class ClipVisionModel(): def encode_image(self, image): img = torch.clip((255. * image), 0, 255).round().int() + img = list(map(lambda a: a, img)) inputs = self.processor(images=img, return_tensors="pt") outputs = self.model(**inputs) return outputs diff --git a/comfy/gligen.py b/comfy/gligen.py index 90558785b..8d182839e 100644 --- a/comfy/gligen.py +++ b/comfy/gligen.py @@ -244,30 +244,15 @@ class Gligen(nn.Module): self.position_net = position_net self.key_dim = key_dim self.max_objs = 30 - self.lowvram = False + self.current_device = torch.device("cpu") def _set_position(self, boxes, masks, positive_embeddings): - if self.lowvram == True: - self.position_net.to(boxes.device) - objs = self.position_net(boxes, masks, positive_embeddings) - - if self.lowvram == True: - self.position_net.cpu() - def func_lowvram(x, extra_options): - key = extra_options["transformer_index"] - module = self.module_list[key] - module.to(x.device) - r = module(x, objs) - module.cpu() - return r - return func_lowvram - else: - def func(x, extra_options): - key = extra_options["transformer_index"] - module = self.module_list[key] - return module(x, objs) - return func + def func(x, extra_options): + key = extra_options["transformer_index"] + module = self.module_list[key] + return module(x, objs) + return func def set_position(self, latent_image_shape, position_params, device): batch, c, h, w = latent_image_shape @@ -312,14 +297,6 @@ class Gligen(nn.Module): masks.to(device), conds.to(device)) - def set_lowvram(self, value=True): - self.lowvram = value - - def cleanup(self): - self.lowvram = False - - def get_models(self): - return [self] def load_gligen(sd): sd_k = sd.keys() diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py index beaa623f3..eb088d92b 100644 --- a/comfy/k_diffusion/sampling.py +++ b/comfy/k_diffusion/sampling.py @@ -649,7 +649,7 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl s_in = x.new_ones([x.shape[0]]) denoised_1, denoised_2 = None, None - h_1, h_2 = None, None + h, h_1, h_2 = None, None, None for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) diff --git a/comfy/model_detection.py b/comfy/model_detection.py index 49ee9ea70..0edc4f180 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -121,9 +121,20 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_fp16): return model_config_from_unet_config(unet_config) -def model_config_from_diffusers_unet(state_dict, use_fp16): +def unet_config_from_diffusers_unet(state_dict, use_fp16): match = {} - match["context_dim"] = state_dict["down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k.weight"].shape[1] + attention_resolutions = [] + + attn_res = 1 + for i in range(5): + k = "down_blocks.{}.attentions.1.transformer_blocks.0.attn2.to_k.weight".format(i) + if k in state_dict: + match["context_dim"] = state_dict[k].shape[1] + attention_resolutions.append(attn_res) + attn_res *= 2 + + match["attention_resolutions"] = attention_resolutions + match["model_channels"] = state_dict["conv_in.weight"].shape[0] match["in_channels"] = state_dict["conv_in.weight"].shape[1] match["adm_in_channels"] = None @@ -135,22 +146,22 @@ def model_config_from_diffusers_unet(state_dict, use_fp16): SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': 2, 'attention_resolutions': [2, 4], 'transformer_depth': [0, 2, 10], 'channel_mult': [1, 2, 4], - 'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048} + 'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048, "num_head_channels": 64} SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2560, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 384, 'num_res_blocks': 2, 'attention_resolutions': [2, 4], 'transformer_depth': [0, 4, 4, 0], 'channel_mult': [1, 2, 4, 4], - 'transformer_depth_middle': 4, 'use_linear_in_transformer': True, 'context_dim': 1280} + 'transformer_depth_middle': 4, 'use_linear_in_transformer': True, 'context_dim': 1280, "num_head_channels": 64} SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': 2, 'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4], - 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024} + 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, "num_head_channels": 64} SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2048, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': 2, 'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4], - 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024} + 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, "num_head_channels": 64} SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 1536, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320, @@ -160,9 +171,20 @@ def model_config_from_diffusers_unet(state_dict, use_fp16): SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': 2, 'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4], - 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768} + 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, "num_heads": 8} - supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl] + SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': 2, 'attention_resolutions': [4], 'transformer_depth': [0, 0, 1], 'channel_mult': [1, 2, 4], + 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 2048, "num_head_channels": 64} + + SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': 2, 'attention_resolutions': [], 'transformer_depth': [0, 0, 0], 'channel_mult': [1, 2, 4], + 'transformer_depth_middle': 0, 'use_linear_in_transformer': True, "num_head_channels": 64, 'context_dim': 1} + + + supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet] for unet_config in supported_models: matches = True @@ -171,5 +193,11 @@ def model_config_from_diffusers_unet(state_dict, use_fp16): matches = False break if matches: - return model_config_from_unet_config(unet_config) + return unet_config + return None + +def model_config_from_diffusers_unet(state_dict, use_fp16): + unet_config = unet_config_from_diffusers_unet(state_dict, use_fp16) + if unet_config is not None: + return model_config_from_unet_config(unet_config) return None diff --git a/comfy/model_management.py b/comfy/model_management.py index 4dd15b41c..5c5d5ab74 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -2,6 +2,7 @@ import psutil from enum import Enum from comfy.cli_args import args import torch +import sys class VRAMState(Enum): DISABLED = 0 #No vram present: no need to move models to vram @@ -201,6 +202,10 @@ if cpu_state == CPUState.MPS: print(f"Set vram state to: {vram_state.name}") +DISABLE_SMART_MEMORY = args.disable_smart_memory + +if DISABLE_SMART_MEMORY: + print("Disabling smart memory management") def get_torch_device_name(device): if hasattr(device, 'type'): @@ -221,132 +226,164 @@ except: print("Could not pick default device.") -current_loaded_model = None -current_gpu_controlnets = [] +current_loaded_models = [] -model_accelerated = False +class LoadedModel: + def __init__(self, model): + self.model = model + self.model_accelerated = False + self.device = model.load_device + def model_memory(self): + return self.model.model_size() -def unload_model(): - global current_loaded_model - global model_accelerated - global current_gpu_controlnets - global vram_state + def model_memory_required(self, device): + if device == self.model.current_device: + return 0 + else: + return self.model_memory() - if current_loaded_model is not None: - if model_accelerated: - accelerate.hooks.remove_hook_from_submodules(current_loaded_model.model) - model_accelerated = False + def model_load(self, lowvram_model_memory=0): + patch_model_to = None + if lowvram_model_memory == 0: + patch_model_to = self.device - current_loaded_model.unpatch_model() - current_loaded_model.model.to(current_loaded_model.offload_device) - current_loaded_model.model_patches_to(current_loaded_model.offload_device) - current_loaded_model = None - if vram_state != VRAMState.HIGH_VRAM: - soft_empty_cache() + self.model.model_patches_to(self.device) + self.model.model_patches_to(self.model.model_dtype()) - if vram_state != VRAMState.HIGH_VRAM: - if len(current_gpu_controlnets) > 0: - for n in current_gpu_controlnets: - n.cpu() - current_gpu_controlnets = [] + try: + self.real_model = self.model.patch_model(device_to=patch_model_to) #TODO: do something with loras and offloading to CPU + except Exception as e: + self.model.unpatch_model(self.model.offload_device) + self.model_unload() + raise e + + if lowvram_model_memory > 0: + print("loading in lowvram mode", lowvram_model_memory/(1024 * 1024)) + device_map = accelerate.infer_auto_device_map(self.real_model, max_memory={0: "{}MiB".format(lowvram_model_memory // (1024 * 1024)), "cpu": "16GiB"}) + accelerate.dispatch_model(self.real_model, device_map=device_map, main_device=self.device) + self.model_accelerated = True + + return self.real_model + + def model_unload(self): + if self.model_accelerated: + accelerate.hooks.remove_hook_from_submodules(self.real_model) + self.model_accelerated = False + + self.model.unpatch_model(self.model.offload_device) + self.model.model_patches_to(self.model.offload_device) + + def __eq__(self, other): + return self.model is other.model def minimum_inference_memory(): - return (768 * 1024 * 1024) + return (1024 * 1024 * 1024) + +def unload_model_clones(model): + to_unload = [] + for i in range(len(current_loaded_models)): + if model.is_clone(current_loaded_models[i].model): + to_unload = [i] + to_unload + + for i in to_unload: + print("unload clone", i) + current_loaded_models.pop(i).model_unload() + +def free_memory(memory_required, device, keep_loaded=[]): + unloaded_model = False + for i in range(len(current_loaded_models) -1, -1, -1): + if DISABLE_SMART_MEMORY: + current_free_mem = 0 + else: + current_free_mem = get_free_memory(device) + if current_free_mem > memory_required: + break + shift_model = current_loaded_models[i] + if shift_model.device == device: + if shift_model not in keep_loaded: + current_loaded_models.pop(i).model_unload() + unloaded_model = True + + if unloaded_model: + soft_empty_cache() + + +def load_models_gpu(models, memory_required=0): + global vram_state + + inference_memory = minimum_inference_memory() + extra_mem = max(inference_memory, memory_required) + + models_to_load = [] + models_already_loaded = [] + for x in models: + loaded_model = LoadedModel(x) + + if loaded_model in current_loaded_models: + index = current_loaded_models.index(loaded_model) + current_loaded_models.insert(0, current_loaded_models.pop(index)) + models_already_loaded.append(loaded_model) + else: + models_to_load.append(loaded_model) + + if len(models_to_load) == 0: + devs = set(map(lambda a: a.device, models_already_loaded)) + for d in devs: + if d != torch.device("cpu"): + free_memory(extra_mem, d, models_already_loaded) + return + + print("loading new") + + total_memory_required = {} + for loaded_model in models_to_load: + unload_model_clones(loaded_model.model) + total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device) + + for device in total_memory_required: + if device != torch.device("cpu"): + free_memory(total_memory_required[device] * 1.3 + extra_mem, device, models_already_loaded) + + for loaded_model in models_to_load: + model = loaded_model.model + torch_dev = model.load_device + if is_device_cpu(torch_dev): + vram_set_state = VRAMState.DISABLED + else: + vram_set_state = vram_state + lowvram_model_memory = 0 + if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM): + model_size = loaded_model.model_memory_required(torch_dev) + current_free_mem = get_free_memory(torch_dev) + lowvram_model_memory = int(max(256 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 )) + if model_size > (current_free_mem - inference_memory): #only switch to lowvram if really necessary + vram_set_state = VRAMState.LOW_VRAM + else: + lowvram_model_memory = 0 + + if vram_set_state == VRAMState.NO_VRAM: + lowvram_model_memory = 256 * 1024 * 1024 + + cur_loaded_model = loaded_model.model_load(lowvram_model_memory) + current_loaded_models.insert(0, loaded_model) + return + def load_model_gpu(model): - global current_loaded_model - global vram_state - global model_accelerated + return load_models_gpu([model]) - if model is current_loaded_model: - return - unload_model() +def cleanup_models(): + to_delete = [] + for i in range(len(current_loaded_models)): + print(sys.getrefcount(current_loaded_models[i].model)) + if sys.getrefcount(current_loaded_models[i].model) <= 2: + to_delete = [i] + to_delete - torch_dev = model.load_device - model.model_patches_to(torch_dev) - model.model_patches_to(model.model_dtype()) - current_loaded_model = model - - if is_device_cpu(torch_dev): - vram_set_state = VRAMState.DISABLED - else: - vram_set_state = vram_state - - if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM): - model_size = model.model_size() - current_free_mem = get_free_memory(torch_dev) - lowvram_model_memory = int(max(256 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 )) - if model_size > (current_free_mem - minimum_inference_memory()): #only switch to lowvram if really necessary - vram_set_state = VRAMState.LOW_VRAM - - real_model = model.model - patch_model_to = None - if vram_set_state == VRAMState.DISABLED: - pass - elif vram_set_state == VRAMState.NORMAL_VRAM or vram_set_state == VRAMState.HIGH_VRAM or vram_set_state == VRAMState.SHARED: - model_accelerated = False - patch_model_to = torch_dev - - try: - real_model = model.patch_model(device_to=patch_model_to) - except Exception as e: - model.unpatch_model() - unload_model() - raise e - - if patch_model_to is not None: - real_model.to(torch_dev) - - if vram_set_state == VRAMState.NO_VRAM: - device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "256MiB", "cpu": "16GiB"}) - accelerate.dispatch_model(real_model, device_map=device_map, main_device=torch_dev) - model_accelerated = True - elif vram_set_state == VRAMState.LOW_VRAM: - device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "{}MiB".format(lowvram_model_memory // (1024 * 1024)), "cpu": "16GiB"}) - accelerate.dispatch_model(real_model, device_map=device_map, main_device=torch_dev) - model_accelerated = True - - return current_loaded_model - -def load_controlnet_gpu(control_models): - global current_gpu_controlnets - global vram_state - if vram_state == VRAMState.DISABLED: - return - - if vram_state == VRAMState.LOW_VRAM or vram_state == VRAMState.NO_VRAM: - for m in control_models: - if hasattr(m, 'set_lowvram'): - m.set_lowvram(True) - #don't load controlnets like this if low vram because they will be loaded right before running and unloaded right after - return - - models = [] - for m in control_models: - models += m.get_models() - - for m in current_gpu_controlnets: - if m not in models: - m.cpu() - - device = get_torch_device() - current_gpu_controlnets = [] - for m in models: - current_gpu_controlnets.append(m.to(device)) - - -def load_if_low_vram(model): - global vram_state - if vram_state == VRAMState.LOW_VRAM or vram_state == VRAMState.NO_VRAM: - return model.to(get_torch_device()) - return model - -def unload_if_low_vram(model): - global vram_state - if vram_state == VRAMState.LOW_VRAM or vram_state == VRAMState.NO_VRAM: - return model.cpu() - return model + for i in to_delete: + x = current_loaded_models.pop(i) + x.model_unload() + del x def unet_offload_device(): if vram_state == VRAMState.HIGH_VRAM: @@ -354,6 +391,25 @@ def unet_offload_device(): else: return torch.device("cpu") +def unet_inital_load_device(parameters, dtype): + torch_dev = get_torch_device() + if vram_state == VRAMState.HIGH_VRAM: + return torch_dev + + cpu_dev = torch.device("cpu") + dtype_size = 4 + if dtype == torch.float16 or dtype == torch.bfloat16: + dtype_size = 2 + + model_size = dtype_size * parameters + + mem_dev = get_free_memory(torch_dev) + mem_cpu = get_free_memory(cpu_dev) + if mem_dev > mem_cpu and model_size < mem_dev: + return torch_dev + else: + return cpu_dev + def text_encoder_offload_device(): if args.gpu_only: return get_torch_device() @@ -456,6 +512,13 @@ def get_free_memory(dev=None, torch_free_too=False): else: return mem_free_total +def batch_area_memory(area): + if xformers_enabled() or pytorch_attention_flash_attention(): + #TODO: these formulas are copied from maximum_batch_area below + return (area / 20) * (1024 * 1024) + else: + return (((area * 0.6) / 0.9) + 1024) * (1024 * 1024) + def maximum_batch_area(): global vram_state if vram_state == VRAMState.NO_VRAM: diff --git a/comfy/sample.py b/comfy/sample.py index 48530f132..d7292024e 100644 --- a/comfy/sample.py +++ b/comfy/sample.py @@ -51,19 +51,24 @@ def get_models_from_cond(cond, model_type): models += [c[1][model_type]] return models -def load_additional_models(positive, negative, dtype): +def get_additional_models(positive, negative): """loads additional models in positive and negative conditioning""" - control_nets = get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control") + control_nets = set(get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control")) + + control_models = [] + for m in control_nets: + control_models += m.get_models() + gligen = get_models_from_cond(positive, "gligen") + get_models_from_cond(negative, "gligen") - gligen = [x[1].to(dtype) for x in gligen] - models = control_nets + gligen - comfy.model_management.load_controlnet_gpu(models) + gligen = [x[1] for x in gligen] + models = control_models + gligen return models def cleanup_additional_models(models): """cleanup additional models that were loaded""" for m in models: - m.cleanup() + if hasattr(m, 'cleanup'): + m.cleanup() def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): device = comfy.model_management.get_torch_device() @@ -72,7 +77,8 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative noise_mask = prepare_mask(noise_mask, noise.shape, device) real_model = None - comfy.model_management.load_model_gpu(model) + models = get_additional_models(positive, negative) + comfy.model_management.load_models_gpu([model] + models, comfy.model_management.batch_area_memory(noise.shape[0] * noise.shape[2] * noise.shape[3])) real_model = model.model noise = noise.to(device) @@ -81,7 +87,6 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative positive_copy = broadcast_cond(positive, noise.shape[0], device) negative_copy = broadcast_cond(negative, noise.shape[0], device) - models = load_additional_models(positive, negative, model.model_dtype()) sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) diff --git a/comfy/samplers.py b/comfy/samplers.py index 28cd46667..ee37913e6 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -88,9 +88,9 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con gligen_type = gligen[0] gligen_model = gligen[1] if gligen_type == "position": - gligen_patch = gligen_model.set_position(input_x.shape, gligen[2], input_x.device) + gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device) else: - gligen_patch = gligen_model.set_empty(input_x.shape, input_x.device) + gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device) patches['middle_patch'] = [gligen_patch] diff --git a/comfy/sd.py b/comfy/sd.py index bff9ee141..461c234db 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -244,7 +244,7 @@ def set_attr(obj, attr, value): del prev class ModelPatcher: - def __init__(self, model, load_device, offload_device, size=0): + def __init__(self, model, load_device, offload_device, size=0, current_device=None): self.size = size self.model = model self.patches = {} @@ -253,6 +253,10 @@ class ModelPatcher: self.model_size() self.load_device = load_device self.offload_device = offload_device + if current_device is None: + self.current_device = self.offload_device + else: + self.current_device = current_device def model_size(self): if self.size > 0: @@ -267,7 +271,7 @@ class ModelPatcher: return size def clone(self): - n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size) + n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device) n.patches = {} for k in self.patches: n.patches[k] = self.patches[k][:] @@ -276,6 +280,11 @@ class ModelPatcher: n.model_keys = self.model_keys return n + def is_clone(self, other): + if hasattr(other, 'model') and self.model is other.model: + return True + return False + def set_model_sampler_cfg_function(self, sampler_cfg_function): if len(inspect.signature(sampler_cfg_function).parameters) == 3: self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way @@ -390,6 +399,11 @@ class ModelPatcher: out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype) set_attr(self.model, key, out_weight) del temp_weight + + if device_to is not None: + self.model.to(device_to) + self.current_device = device_to + return self.model def calculate_weight(self, patches, weight, key): @@ -482,7 +496,7 @@ class ModelPatcher: return weight - def unpatch_model(self): + def unpatch_model(self, device_to=None): keys = list(self.backup.keys()) for k in keys: @@ -490,6 +504,11 @@ class ModelPatcher: self.backup = {} + if device_to is not None: + self.model.to(device_to) + self.current_device = device_to + + def load_lora_for_models(model, clip, lora, strength_model, strength_clip): key_map = model_lora_keys_unet(model.model) key_map = model_lora_keys_clip(clip.cond_stage_model, key_map) @@ -555,7 +574,7 @@ class CLIP: else: self.cond_stage_model.reset_clip_layer() - model_management.load_model_gpu(self.patcher) + self.load_model() cond, pooled = self.cond_stage_model.encode_token_weights(tokens) if return_pooled: return cond, pooled @@ -571,11 +590,9 @@ class CLIP: def get_sd(self): return self.cond_stage_model.state_dict() - def patch_model(self): - self.patcher.patch_model() - - def unpatch_model(self): - self.patcher.unpatch_model() + def load_model(self): + model_management.load_model_gpu(self.patcher) + return self.patcher def get_key_patches(self): return self.patcher.get_key_patches() @@ -630,11 +647,12 @@ class VAE: return samples def decode(self, samples_in): - model_management.unload_model() self.first_stage_model = self.first_stage_model.to(self.device) try: + memory_used = (2562 * samples_in.shape[2] * samples_in.shape[3] * 64) * 1.4 + model_management.free_memory(memory_used, self.device) free_memory = model_management.get_free_memory(self.device) - batch_number = int((free_memory * 0.7) / (2562 * samples_in.shape[2] * samples_in.shape[3] * 64)) + batch_number = int(free_memory / memory_used) batch_number = max(1, batch_number) pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * 8), round(samples_in.shape[3] * 8)), device="cpu") @@ -650,19 +668,19 @@ class VAE: return pixel_samples def decode_tiled(self, samples, tile_x=64, tile_y=64, overlap = 16): - model_management.unload_model() self.first_stage_model = self.first_stage_model.to(self.device) output = self.decode_tiled_(samples, tile_x, tile_y, overlap) self.first_stage_model = self.first_stage_model.to(self.offload_device) return output.movedim(1,-1) def encode(self, pixel_samples): - model_management.unload_model() self.first_stage_model = self.first_stage_model.to(self.device) pixel_samples = pixel_samples.movedim(-1,1) try: + memory_used = (2078 * pixel_samples.shape[2] * pixel_samples.shape[3]) * 1.4 #NOTE: this constant along with the one in the decode above are estimated from the mem usage for the VAE and could change. + model_management.free_memory(memory_used, self.device) free_memory = model_management.get_free_memory(self.device) - batch_number = int((free_memory * 0.7) / (2078 * pixel_samples.shape[2] * pixel_samples.shape[3])) #NOTE: this constant along with the one in the decode above are estimated from the mem usage for the VAE and could change. + batch_number = int(free_memory / memory_used) batch_number = max(1, batch_number) samples = torch.empty((pixel_samples.shape[0], 4, round(pixel_samples.shape[2] // 8), round(pixel_samples.shape[3] // 8)), device="cpu") for x in range(0, pixel_samples.shape[0], batch_number): @@ -677,7 +695,6 @@ class VAE: return samples def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): - model_management.unload_model() self.first_stage_model = self.first_stage_model.to(self.device) pixel_samples = pixel_samples.movedim(-1,1) samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap) @@ -757,6 +774,7 @@ class ControlNet(ControlBase): def __init__(self, control_model, global_average_pooling=False, device=None): super().__init__(device) self.control_model = control_model + self.control_model_wrapped = ModelPatcher(self.control_model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device()) self.global_average_pooling = global_average_pooling def get_control(self, x_noisy, t, cond, batched_number): @@ -786,11 +804,9 @@ class ControlNet(ControlBase): precision_scope = contextlib.nullcontext with precision_scope(model_management.get_autocast_device(self.device)): - self.control_model = model_management.load_if_low_vram(self.control_model) context = torch.cat(cond['c_crossattn'], 1) y = cond.get('c_adm', None) control = self.control_model(x=x_noisy, hint=self.cond_hint, timesteps=t, context=context, y=y) - self.control_model = model_management.unload_if_low_vram(self.control_model) out = {'middle':[], 'output': []} autocast_enabled = torch.is_autocast_enabled() @@ -825,7 +841,7 @@ class ControlNet(ControlBase): def get_models(self): out = super().get_models() - out.append(self.control_model) + out.append(self.control_model_wrapped) return out @@ -835,7 +851,7 @@ def load_controlnet(ckpt_path, model=None): controlnet_config = None if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format use_fp16 = model_management.should_use_fp16() - controlnet_config = model_detection.model_config_from_diffusers_unet(controlnet_data, use_fp16).unet_config + controlnet_config = model_detection.unet_config_from_diffusers_unet(controlnet_data, use_fp16) diffusers_keys = utils.unet_to_diffusers(controlnet_config) diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight" diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias" @@ -874,6 +890,9 @@ def load_controlnet(ckpt_path, model=None): if k in controlnet_data: new_sd[diffusers_keys[k]] = controlnet_data.pop(k) + leftover_keys = controlnet_data.keys() + if len(leftover_keys) > 0: + print("leftover keys:", leftover_keys) controlnet_data = new_sd pth_key = 'control_model.zero_convs.0.0.weight' @@ -901,8 +920,8 @@ def load_controlnet(ckpt_path, model=None): if pth: if 'difference' in controlnet_data: if model is not None: - m = model.patch_model() - model_sd = m.state_dict() + model_management.load_models_gpu([model]) + model_sd = model.model_state_dict() for x in controlnet_data: c_m = "control_model." if x.startswith(c_m): @@ -910,7 +929,6 @@ def load_controlnet(ckpt_path, model=None): if sd_key in model_sd: cd = controlnet_data[x] cd += model_sd[sd_key].type(cd.dtype).to(cd.device) - model.unpatch_model() else: print("WARNING: Loaded a diff controlnet without a model. It will very likely not work.") @@ -1001,7 +1019,6 @@ class T2IAdapter(ControlBase): self.copy_to(c) return c - def load_t2i_adapter(t2i_data): keys = t2i_data.keys() if 'adapter' in keys: @@ -1087,7 +1104,7 @@ def load_gligen(ckpt_path): model = gligen.load_gligen(data) if model_management.should_use_fp16(): model = model.half() - return model + return ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device()) def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None): #TODO: this function is a mess and should be removed eventually @@ -1199,8 +1216,13 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o if output_clipvision: clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True) + dtype = torch.float32 + if fp16: + dtype = torch.float16 + + inital_load_device = model_management.unet_inital_load_device(parameters, dtype) offload_device = model_management.unet_offload_device() - model = model_config.get_model(sd, "model.diffusion_model.", device=offload_device) + model = model_config.get_model(sd, "model.diffusion_model.", device=inital_load_device) model.load_model_weights(sd, "model.diffusion_model.") if output_vae: @@ -1221,7 +1243,12 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o if len(left_over) > 0: print("left over keys:", left_over) - return (ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae, clipvision) + model_patcher = ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device(), current_device=inital_load_device) + if inital_load_device != torch.device("cpu"): + print("loaded straight to GPU") + model_management.load_model_gpu(model_patcher) + + return (model_patcher, clip, vae, clipvision) def load_unet(unet_path): #load unet in diffusers format @@ -1249,14 +1276,6 @@ def load_unet(unet_path): #load unet in diffusers format return ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device) def save_checkpoint(output_path, model, clip, vae, metadata=None): - try: - model.patch_model() - clip.patch_model() - sd = model.model.state_dict_for_saving(clip.get_sd(), vae.get_sd()) - utils.save_torch_file(sd, output_path, metadata=metadata) - model.unpatch_model() - clip.unpatch_model() - except Exception as e: - model.unpatch_model() - clip.unpatch_model() - raise e + model_management.load_models_gpu([model, clip.load_model()]) + sd = model.model.state_dict_for_saving(clip.get_sd(), vae.get_sd()) + utils.save_torch_file(sd, output_path, metadata=metadata) diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py index b80c8b9a2..5adb468ac 100644 --- a/comfy_extras/nodes_mask.py +++ b/comfy_extras/nodes_mask.py @@ -1,15 +1,19 @@ +import numpy as np +from scipy.ndimage import grey_dilation import torch from nodes import MAX_RESOLUTION -def composite(destination, source, x, y, mask = None, multiplier = 8): +def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False): + if resize_source: + source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear") + x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier)) y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier)) left, top = (x // multiplier, y // multiplier) right, bottom = (left + source.shape[3], top + source.shape[2],) - if mask is None: mask = torch.ones_like(source) else: @@ -40,6 +44,7 @@ class LatentCompositeMasked: "source": ("LATENT",), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "resize_source": ("BOOLEAN", {"default": False}), }, "optional": { "mask": ("MASK",), @@ -50,11 +55,11 @@ class LatentCompositeMasked: CATEGORY = "latent" - def composite(self, destination, source, x, y, mask = None): + def composite(self, destination, source, x, y, resize_source, mask = None): output = destination.copy() destination = destination["samples"].clone() source = source["samples"] - output["samples"] = composite(destination, source, x, y, mask, 8) + output["samples"] = composite(destination, source, x, y, mask, 8, resize_source) return (output,) class ImageCompositeMasked: @@ -66,6 +71,7 @@ class ImageCompositeMasked: "source": ("IMAGE",), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "resize_source": ("BOOLEAN", {"default": False}), }, "optional": { "mask": ("MASK",), @@ -76,9 +82,9 @@ class ImageCompositeMasked: CATEGORY = "image" - def composite(self, destination, source, x, y, mask = None): + def composite(self, destination, source, x, y, resize_source, mask = None): destination = destination.clone().movedim(-1, 1) - output = composite(destination, source.movedim(-1, 1), x, y, mask, 1).movedim(1, -1) + output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1) return (output,) class MaskToImage: @@ -272,6 +278,35 @@ class FeatherMask: output[-y, :] *= feather_rate return (output,) + +class GrowMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK",), + "expand": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "tapered_corners": ("BOOLEAN", {"default": True}), + }, + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "expand_mask" + + def expand_mask(self, mask, expand, tapered_corners): + c = 0 if tapered_corners else 1 + kernel = np.array([[c, 1, c], + [1, 1, 1], + [c, 1, c]]) + output = mask.numpy().copy() + while expand > 0: + output = grey_dilation(output, footprint=kernel) + expand -= 1 + output = torch.from_numpy(output) + return (output,) @@ -285,6 +320,7 @@ NODE_CLASS_MAPPINGS = { "CropMask": CropMask, "MaskComposite": MaskComposite, "FeatherMask": FeatherMask, + "GrowMask": GrowMask, } NODE_DISPLAY_NAME_MAPPINGS = { diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py index a138b292e..51bdb24fa 100644 --- a/comfy_extras/nodes_post_processing.py +++ b/comfy_extras/nodes_post_processing.py @@ -2,6 +2,7 @@ import numpy as np import torch import torch.nn.functional as F from PIL import Image +import math import comfy.utils @@ -209,9 +210,36 @@ class Sharpen: return (result,) +class ImageScaleToTotalPixels: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic"] + crop_methods = ["disabled", "center"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), + "megapixels": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 16.0, "step": 0.01}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + + def upscale(self, image, upscale_method, megapixels): + samples = image.movedim(-1,1) + total = int(megapixels * 1024 * 1024) + + scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) + width = round(samples.shape[3] * scale_by) + height = round(samples.shape[2] * scale_by) + + s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled") + s = s.movedim(1,-1) + return (s,) + NODE_CLASS_MAPPINGS = { "ImageBlend": Blend, "ImageBlur": Blur, "ImageQuantize": Quantize, "ImageSharpen": Sharpen, + "ImageScaleToTotalPixels": ImageScaleToTotalPixels, } diff --git a/execution.py b/execution.py index a1a7c75c8..e10fdbb60 100644 --- a/execution.py +++ b/execution.py @@ -354,6 +354,7 @@ class PromptExecutor: d = self.outputs_ui.pop(x) del d + comfy.model_management.cleanup_models() if self.server.client_id is not None: self.server.send_sync("execution_cached", { "nodes": list(current_outputs) , "prompt_id": prompt_id}, self.server.client_id) executed = set() diff --git a/nodes.py b/nodes.py index f9057c97b..057a7e95f 100644 --- a/nodes.py +++ b/nodes.py @@ -1588,6 +1588,28 @@ class ImageBatch: s = torch.cat((image1, image2), dim=0) return (s,) +class EmptyImage: + def __init__(self, device="cpu"): + self.device = device + + @classmethod + def INPUT_TYPES(s): + return {"required": { "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}), + "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "generate" + + CATEGORY = "image" + + def generate(self, width, height, batch_size=1, color=0): + r = torch.full([batch_size, height, width, 1], ((color >> 16) & 0xFF) / 0xFF) + g = torch.full([batch_size, height, width, 1], ((color >> 8) & 0xFF) / 0xFF) + b = torch.full([batch_size, height, width, 1], ((color) & 0xFF) / 0xFF) + return (torch.cat((r, g, b), dim=-1), ) + class ImagePadForOutpaint: @classmethod @@ -1674,6 +1696,7 @@ NODE_CLASS_MAPPINGS = { "ImageInvert": ImageInvert, "ImageBatch": ImageBatch, "ImagePadForOutpaint": ImagePadForOutpaint, + "EmptyImage": EmptyImage, "ConditioningAverage ": ConditioningAverage , "ConditioningCombine": ConditioningCombine, "ConditioningConcat": ConditioningConcat, diff --git a/web/index.html b/web/index.html index 71067d993..41bc246c0 100644 --- a/web/index.html +++ b/web/index.html @@ -6,6 +6,7 @@ +