import torch import contextlib import copy import inspect from comfy import model_management from .ldm.util import instantiate_from_config from .ldm.models.autoencoder import AutoencoderKL import yaml from .cldm import cldm from .t2i_adapter import adapter from . import utils from . import clip_vision from . import gligen from . import diffusers_convert from . import model_base from . import model_detection from . import sd1_clip from . import sd2_clip from . import sdxl_clip def load_model_weights(model, sd): m, u = model.load_state_dict(sd, strict=False) m = set(m) unexpected_keys = set(u) k = list(sd.keys()) for x in k: if x not in unexpected_keys: w = sd.pop(x) del w if len(m) > 0: print("missing", m) return model def load_clip_weights(model, sd): k = list(sd.keys()) for x in k: if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."): y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.") sd[y] = sd.pop(x) if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in sd: ids = sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] if ids.dtype == torch.float32: sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round() sd = utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24) return load_model_weights(model, sd) LORA_CLIP_MAP = { "mlp.fc1": "mlp_fc1", "mlp.fc2": "mlp_fc2", "self_attn.k_proj": "self_attn_k_proj", "self_attn.q_proj": "self_attn_q_proj", "self_attn.v_proj": "self_attn_v_proj", "self_attn.out_proj": "self_attn_out_proj", } def load_lora(lora, to_load): patch_dict = {} loaded_keys = set() for x in to_load: alpha_name = "{}.alpha".format(x) alpha = None if alpha_name in lora.keys(): alpha = lora[alpha_name].item() loaded_keys.add(alpha_name) A_name = "{}.lora_up.weight".format(x) B_name = "{}.lora_down.weight".format(x) mid_name = "{}.lora_mid.weight".format(x) if A_name in lora.keys(): mid = None if mid_name in lora.keys(): mid = lora[mid_name] loaded_keys.add(mid_name) patch_dict[to_load[x]] = (lora[A_name], lora[B_name], alpha, mid) loaded_keys.add(A_name) loaded_keys.add(B_name) ######## loha hada_w1_a_name = "{}.hada_w1_a".format(x) hada_w1_b_name = "{}.hada_w1_b".format(x) hada_w2_a_name = "{}.hada_w2_a".format(x) hada_w2_b_name = "{}.hada_w2_b".format(x) hada_t1_name = "{}.hada_t1".format(x) hada_t2_name = "{}.hada_t2".format(x) if hada_w1_a_name in lora.keys(): hada_t1 = None hada_t2 = None if hada_t1_name in lora.keys(): hada_t1 = lora[hada_t1_name] hada_t2 = lora[hada_t2_name] loaded_keys.add(hada_t1_name) loaded_keys.add(hada_t2_name) patch_dict[to_load[x]] = (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2) loaded_keys.add(hada_w1_a_name) loaded_keys.add(hada_w1_b_name) loaded_keys.add(hada_w2_a_name) loaded_keys.add(hada_w2_b_name) ######## lokr lokr_w1_name = "{}.lokr_w1".format(x) lokr_w2_name = "{}.lokr_w2".format(x) lokr_w1_a_name = "{}.lokr_w1_a".format(x) lokr_w1_b_name = "{}.lokr_w1_b".format(x) lokr_t2_name = "{}.lokr_t2".format(x) lokr_w2_a_name = "{}.lokr_w2_a".format(x) lokr_w2_b_name = "{}.lokr_w2_b".format(x) lokr_w1 = None if lokr_w1_name in lora.keys(): lokr_w1 = lora[lokr_w1_name] loaded_keys.add(lokr_w1_name) lokr_w2 = None if lokr_w2_name in lora.keys(): lokr_w2 = lora[lokr_w2_name] loaded_keys.add(lokr_w2_name) lokr_w1_a = None if lokr_w1_a_name in lora.keys(): lokr_w1_a = lora[lokr_w1_a_name] loaded_keys.add(lokr_w1_a_name) lokr_w1_b = None if lokr_w1_b_name in lora.keys(): lokr_w1_b = lora[lokr_w1_b_name] loaded_keys.add(lokr_w1_b_name) lokr_w2_a = None if lokr_w2_a_name in lora.keys(): lokr_w2_a = lora[lokr_w2_a_name] loaded_keys.add(lokr_w2_a_name) lokr_w2_b = None if lokr_w2_b_name in lora.keys(): lokr_w2_b = lora[lokr_w2_b_name] loaded_keys.add(lokr_w2_b_name) lokr_t2 = None if lokr_t2_name in lora.keys(): lokr_t2 = lora[lokr_t2_name] loaded_keys.add(lokr_t2_name) if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): patch_dict[to_load[x]] = (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2) for x in lora.keys(): if x not in loaded_keys: print("lora key not loaded", x) return patch_dict def model_lora_keys_clip(model, key_map={}): sdk = model.state_dict().keys() text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}" clip_l_present = False for b in range(32): for c in LORA_CLIP_MAP: k = "transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) if k in sdk: lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) key_map[lora_key] = k k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) if k in sdk: lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base key_map[lora_key] = k clip_l_present = True k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) if k in sdk: if clip_l_present: lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base else: lora_key = "lora_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #TODO: test if this is correct for SDXL-Refiner key_map[lora_key] = k return key_map def model_lora_keys_unet(model, key_map={}): sdk = model.state_dict().keys() for k in sdk: if k.startswith("diffusion_model.") and k.endswith(".weight"): key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") key_map["lora_unet_{}".format(key_lora)] = k diffusers_keys = utils.unet_to_diffusers(model.model_config.unet_config) for k in diffusers_keys: if k.endswith(".weight"): key_lora = k[:-len(".weight")].replace(".", "_") key_map["lora_unet_{}".format(key_lora)] = "diffusion_model.{}".format(diffusers_keys[k]) return key_map class ModelPatcher: def __init__(self, model, load_device, offload_device, size=0): self.size = size self.model = model self.patches = {} self.backup = {} self.model_options = {"transformer_options":{}} self.model_size() self.load_device = load_device self.offload_device = offload_device def model_size(self): if self.size > 0: return self.size model_sd = self.model.state_dict() size = 0 for k in model_sd: t = model_sd[k] size += t.nelement() * t.element_size() self.size = size self.model_keys = set(model_sd.keys()) return size def clone(self): n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size) n.patches = {} for k in self.patches: n.patches[k] = self.patches[k][:] n.model_options = copy.deepcopy(self.model_options) n.model_keys = self.model_keys return n 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 else: self.model_options["sampler_cfg_function"] = sampler_cfg_function def set_model_unet_function_wrapper(self, unet_wrapper_function): self.model_options["model_function_wrapper"] = unet_wrapper_function def set_model_patch(self, patch, name): to = self.model_options["transformer_options"] if "patches" not in to: to["patches"] = {} to["patches"][name] = to["patches"].get(name, []) + [patch] def set_model_patch_replace(self, patch, name, block_name, number): to = self.model_options["transformer_options"] if "patches_replace" not in to: to["patches_replace"] = {} if name not in to["patches_replace"]: to["patches_replace"][name] = {} to["patches_replace"][name][(block_name, number)] = patch def set_model_attn1_patch(self, patch): self.set_model_patch(patch, "attn1_patch") def set_model_attn2_patch(self, patch): self.set_model_patch(patch, "attn2_patch") def set_model_attn1_replace(self, patch, block_name, number): self.set_model_patch_replace(patch, "attn1", block_name, number) def set_model_attn2_replace(self, patch, block_name, number): self.set_model_patch_replace(patch, "attn2", block_name, number) def set_model_attn1_output_patch(self, patch): self.set_model_patch(patch, "attn1_output_patch") def set_model_attn2_output_patch(self, patch): self.set_model_patch(patch, "attn2_output_patch") def model_patches_to(self, device): to = self.model_options["transformer_options"] if "patches" in to: patches = to["patches"] for name in patches: patch_list = patches[name] for i in range(len(patch_list)): if hasattr(patch_list[i], "to"): patch_list[i] = patch_list[i].to(device) if "patches_replace" in to: patches = to["patches_replace"] for name in patches: patch_list = patches[name] for k in patch_list: if hasattr(patch_list[k], "to"): patch_list[k] = patch_list[k].to(device) def model_dtype(self): if hasattr(self.model, "get_dtype"): return self.model.get_dtype() def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): p = set() for k in patches: if k in self.model_keys: p.add(k) current_patches = self.patches.get(k, []) current_patches.append((strength_patch, patches[k], strength_model)) self.patches[k] = current_patches return list(p) def get_key_patches(self, filter_prefix=None): model_sd = self.model_state_dict() p = {} for k in model_sd: if filter_prefix is not None: if not k.startswith(filter_prefix): continue if k in self.patches: p[k] = [model_sd[k]] + self.patches[k] else: p[k] = (model_sd[k],) return p def model_state_dict(self, filter_prefix=None): sd = self.model.state_dict() keys = list(sd.keys()) if filter_prefix is not None: for k in keys: if not k.startswith(filter_prefix): sd.pop(k) return sd def patch_model(self): model_sd = self.model_state_dict() for key in self.patches: if key not in model_sd: print("could not patch. key doesn't exist in model:", k) continue weight = model_sd[key] if key not in self.backup: self.backup[key] = weight.clone() weight[:] = self.calculate_weight(self.patches[key], weight.clone(), key) return self.model def calculate_weight(self, patches, weight, key): for p in patches: alpha = p[0] v = p[1] strength_model = p[2] if strength_model != 1.0: weight *= strength_model if isinstance(v, list): v = (self.calculate_weight(v[1:], v[0].clone(), key), ) if len(v) == 1: w1 = v[0] if w1.shape != weight.shape: print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) else: weight += alpha * w1.type(weight.dtype).to(weight.device) elif len(v) == 4: #lora/locon mat1 = v[0] mat2 = v[1] if v[2] is not None: alpha *= v[2] / mat2.shape[0] if v[3] is not None: #locon mid weights, hopefully the math is fine because I didn't properly test it final_shape = [mat2.shape[1], mat2.shape[0], v[3].shape[2], v[3].shape[3]] mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1).float(), v[3].transpose(0, 1).flatten(start_dim=1).float()).reshape(final_shape).transpose(0, 1) weight += (alpha * torch.mm(mat1.flatten(start_dim=1).float(), mat2.flatten(start_dim=1).float())).reshape(weight.shape).type(weight.dtype).to(weight.device) elif len(v) == 8: #lokr w1 = v[0] w2 = v[1] w1_a = v[3] w1_b = v[4] w2_a = v[5] w2_b = v[6] t2 = v[7] dim = None if w1 is None: dim = w1_b.shape[0] w1 = torch.mm(w1_a.float(), w1_b.float()) if w2 is None: dim = w2_b.shape[0] if t2 is None: w2 = torch.mm(w2_a.float(), w2_b.float()) else: w2 = torch.einsum('i j k l, j r, i p -> p r k l', t2.float(), w2_b.float(), w2_a.float()) if len(w2.shape) == 4: w1 = w1.unsqueeze(2).unsqueeze(2) if v[2] is not None and dim is not None: alpha *= v[2] / dim weight += alpha * torch.kron(w1.float(), w2.float()).reshape(weight.shape).type(weight.dtype).to(weight.device) else: #loha w1a = v[0] w1b = v[1] if v[2] is not None: alpha *= v[2] / w1b.shape[0] w2a = v[3] w2b = v[4] if v[5] is not None: #cp decomposition t1 = v[5] t2 = v[6] m1 = torch.einsum('i j k l, j r, i p -> p r k l', t1.float(), w1b.float(), w1a.float()) m2 = torch.einsum('i j k l, j r, i p -> p r k l', t2.float(), w2b.float(), w2a.float()) else: m1 = torch.mm(w1a.float(), w1b.float()) m2 = torch.mm(w2a.float(), w2b.float()) weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype).to(weight.device) return weight def unpatch_model(self): model_sd = self.model_state_dict() keys = list(self.backup.keys()) for k in keys: model_sd[k][:] = self.backup[k] del self.backup[k] self.backup = {} 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) loaded = load_lora(lora, key_map) new_modelpatcher = model.clone() k = new_modelpatcher.add_patches(loaded, strength_model) new_clip = clip.clone() k1 = new_clip.add_patches(loaded, strength_clip) k = set(k) k1 = set(k1) for x in loaded: if (x not in k) and (x not in k1): print("NOT LOADED", x) return (new_modelpatcher, new_clip) class CLIP: def __init__(self, target=None, embedding_directory=None, no_init=False): if no_init: return params = target.params.copy() clip = target.clip tokenizer = target.tokenizer load_device = model_management.text_encoder_device() offload_device = model_management.text_encoder_offload_device() params['device'] = load_device self.cond_stage_model = clip(**(params)) #TODO: make sure this doesn't have a quality loss before enabling. # if model_management.should_use_fp16(load_device): # self.cond_stage_model.half() self.cond_stage_model = self.cond_stage_model.to() self.tokenizer = tokenizer(embedding_directory=embedding_directory) self.patcher = ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device) self.layer_idx = None def clone(self): n = CLIP(no_init=True) n.patcher = self.patcher.clone() n.cond_stage_model = self.cond_stage_model n.tokenizer = self.tokenizer n.layer_idx = self.layer_idx return n def load_from_state_dict(self, sd): self.cond_stage_model.load_sd(sd) def add_patches(self, patches, strength=1.0): return self.patcher.add_patches(patches, strength) def clip_layer(self, layer_idx): self.layer_idx = layer_idx def tokenize(self, text, return_word_ids=False): return self.tokenizer.tokenize_with_weights(text, return_word_ids) def encode_from_tokens(self, tokens, return_pooled=False): if self.layer_idx is not None: self.cond_stage_model.clip_layer(self.layer_idx) model_management.load_model_gpu(self.patcher) cond, pooled = self.cond_stage_model.encode_token_weights(tokens) if return_pooled: return cond, pooled return cond def encode(self, text): tokens = self.tokenize(text) return self.encode_from_tokens(tokens) def load_sd(self, sd): return self.cond_stage_model.load_sd(sd) 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() class VAE: def __init__(self, ckpt_path=None, device=None, config=None): if config is None: #default SD1.x/SD2.x VAE parameters ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} self.first_stage_model = AutoencoderKL(ddconfig, {'target': 'torch.nn.Identity'}, 4, monitor="val/rec_loss") else: self.first_stage_model = AutoencoderKL(**(config['params'])) self.first_stage_model = self.first_stage_model.eval() if ckpt_path is not None: sd = utils.load_torch_file(ckpt_path) if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format sd = diffusers_convert.convert_vae_state_dict(sd) self.first_stage_model.load_state_dict(sd, strict=False) if device is None: device = model_management.vae_device() self.device = device self.offload_device = model_management.vae_offload_device() self.vae_dtype = model_management.vae_dtype() self.first_stage_model.to(self.vae_dtype) def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16): steps = samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap) steps += samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap) steps += samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap) pbar = utils.ProgressBar(steps) decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)) + 1.0).float() output = torch.clamp(( (utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8, pbar = pbar) + utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8, pbar = pbar) + utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = 8, pbar = pbar)) / 3.0) / 2.0, min=0.0, max=1.0) return output def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): steps = pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap) steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap) pbar = utils.ProgressBar(steps) encode_fn = lambda a: self.first_stage_model.encode(2. * a.to(self.vae_dtype).to(self.device) - 1.).sample().float() samples = utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) samples += utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) samples += utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) samples /= 3.0 return samples def decode(self, samples_in): model_management.unload_model() self.first_stage_model = self.first_stage_model.to(self.device) try: 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 = 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") for x in range(0, samples_in.shape[0], batch_number): samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device) pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples) + 1.0) / 2.0, min=0.0, max=1.0).cpu().float() except model_management.OOM_EXCEPTION as e: print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.") pixel_samples = self.decode_tiled_(samples_in) self.first_stage_model = self.first_stage_model.to(self.offload_device) pixel_samples = pixel_samples.cpu().movedim(1,-1) 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: 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 = 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): pixels_in = (2. * pixel_samples[x:x+batch_number] - 1.).to(self.vae_dtype).to(self.device) samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).sample().cpu().float() except model_management.OOM_EXCEPTION as e: print("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.") samples = self.encode_tiled_(pixel_samples) self.first_stage_model = self.first_stage_model.to(self.offload_device) 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) self.first_stage_model = self.first_stage_model.to(self.offload_device) return samples def get_sd(self): return self.first_stage_model.state_dict() def broadcast_image_to(tensor, target_batch_size, batched_number): current_batch_size = tensor.shape[0] #print(current_batch_size, target_batch_size) if current_batch_size == 1: return tensor per_batch = target_batch_size // batched_number tensor = tensor[:per_batch] if per_batch > tensor.shape[0]: tensor = torch.cat([tensor] * (per_batch // tensor.shape[0]) + [tensor[:(per_batch % tensor.shape[0])]], dim=0) current_batch_size = tensor.shape[0] if current_batch_size == target_batch_size: return tensor else: return torch.cat([tensor] * batched_number, dim=0) class ControlNet: def __init__(self, control_model, global_average_pooling=False, device=None): self.control_model = control_model self.cond_hint_original = None self.cond_hint = None self.strength = 1.0 if device is None: device = model_management.get_torch_device() self.device = device self.previous_controlnet = None self.global_average_pooling = global_average_pooling def get_control(self, x_noisy, t, cond, batched_number): control_prev = None if self.previous_controlnet is not None: control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number) output_dtype = x_noisy.dtype if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]: if self.cond_hint is not None: del self.cond_hint self.cond_hint = None self.cond_hint = utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(self.control_model.dtype).to(self.device) if x_noisy.shape[0] != self.cond_hint.shape[0]: self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) if self.control_model.dtype == torch.float16: precision_scope = torch.autocast else: 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() for i in range(len(control)): if i == (len(control) - 1): key = 'middle' index = 0 else: key = 'output' index = i x = control[i] if self.global_average_pooling: x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3]) x *= self.strength if x.dtype != output_dtype and not autocast_enabled: x = x.to(output_dtype) if control_prev is not None and key in control_prev: prev = control_prev[key][index] if prev is not None: x += prev out[key].append(x) if control_prev is not None and 'input' in control_prev: out['input'] = control_prev['input'] return out def set_cond_hint(self, cond_hint, strength=1.0): self.cond_hint_original = cond_hint self.strength = strength return self def set_previous_controlnet(self, controlnet): self.previous_controlnet = controlnet return self def cleanup(self): if self.previous_controlnet is not None: self.previous_controlnet.cleanup() if self.cond_hint is not None: del self.cond_hint self.cond_hint = None def copy(self): c = ControlNet(self.control_model, global_average_pooling=self.global_average_pooling) c.cond_hint_original = self.cond_hint_original c.strength = self.strength return c def get_models(self): out = [] if self.previous_controlnet is not None: out += self.previous_controlnet.get_models() out.append(self.control_model) return out def load_controlnet(ckpt_path, model=None): controlnet_data = utils.load_torch_file(ckpt_path, safe_load=True) pth_key = 'control_model.zero_convs.0.0.weight' pth = False key = 'zero_convs.0.0.weight' if pth_key in controlnet_data: pth = True key = pth_key prefix = "control_model." elif key in controlnet_data: prefix = "" else: net = load_t2i_adapter(controlnet_data) if net is None: print("error checkpoint does not contain controlnet or t2i adapter data", ckpt_path) return net use_fp16 = model_management.should_use_fp16() controlnet_config = model_detection.model_config_from_unet(controlnet_data, prefix, use_fp16).unet_config controlnet_config.pop("out_channels") controlnet_config["hint_channels"] = 3 control_model = cldm.ControlNet(**controlnet_config) if pth: if 'difference' in controlnet_data: if model is not None: m = model.patch_model() model_sd = m.state_dict() for x in controlnet_data: c_m = "control_model." if x.startswith(c_m): sd_key = "diffusion_model.{}".format(x[len(c_m):]) 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.") class WeightsLoader(torch.nn.Module): pass w = WeightsLoader() w.control_model = control_model missing, unexpected = w.load_state_dict(controlnet_data, strict=False) else: missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False) print(missing, unexpected) if use_fp16: control_model = control_model.half() global_average_pooling = False if ckpt_path.endswith("_shuffle.pth") or ckpt_path.endswith("_shuffle.safetensors") or ckpt_path.endswith("_shuffle_fp16.safetensors"): #TODO: smarter way of enabling global_average_pooling global_average_pooling = True control = ControlNet(control_model, global_average_pooling=global_average_pooling) return control class T2IAdapter: def __init__(self, t2i_model, channels_in, device=None): self.t2i_model = t2i_model self.channels_in = channels_in self.strength = 1.0 if device is None: device = model_management.get_torch_device() self.device = device self.previous_controlnet = None self.control_input = None self.cond_hint_original = None self.cond_hint = None def get_control(self, x_noisy, t, cond, batched_number): control_prev = None if self.previous_controlnet is not None: control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number) if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]: if self.cond_hint is not None: del self.cond_hint self.control_input = None self.cond_hint = None self.cond_hint = utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").float().to(self.device) if self.channels_in == 1 and self.cond_hint.shape[1] > 1: self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True) if x_noisy.shape[0] != self.cond_hint.shape[0]: self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) if self.control_input is None: self.t2i_model.to(self.device) self.control_input = self.t2i_model(self.cond_hint) self.t2i_model.cpu() output_dtype = x_noisy.dtype out = {'input':[]} autocast_enabled = torch.is_autocast_enabled() for i in range(len(self.control_input)): key = 'input' x = self.control_input[i] * self.strength if x.dtype != output_dtype and not autocast_enabled: x = x.to(output_dtype) if control_prev is not None and key in control_prev: index = len(control_prev[key]) - i * 3 - 3 prev = control_prev[key][index] if prev is not None: x += prev out[key].insert(0, None) out[key].insert(0, None) out[key].insert(0, x) if control_prev is not None and 'input' in control_prev: for i in range(len(out['input'])): if out['input'][i] is None: out['input'][i] = control_prev['input'][i] if control_prev is not None and 'middle' in control_prev: out['middle'] = control_prev['middle'] if control_prev is not None and 'output' in control_prev: out['output'] = control_prev['output'] return out def set_cond_hint(self, cond_hint, strength=1.0): self.cond_hint_original = cond_hint self.strength = strength return self def set_previous_controlnet(self, controlnet): self.previous_controlnet = controlnet return self def copy(self): c = T2IAdapter(self.t2i_model, self.channels_in) c.cond_hint_original = self.cond_hint_original c.strength = self.strength return c def cleanup(self): if self.previous_controlnet is not None: self.previous_controlnet.cleanup() if self.cond_hint is not None: del self.cond_hint self.cond_hint = None def get_models(self): out = [] if self.previous_controlnet is not None: out += self.previous_controlnet.get_models() return out def load_t2i_adapter(t2i_data): keys = t2i_data.keys() if 'adapter' in keys: t2i_data = t2i_data['adapter'] keys = t2i_data.keys() if "body.0.in_conv.weight" in keys: cin = t2i_data['body.0.in_conv.weight'].shape[1] model_ad = adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4) elif 'conv_in.weight' in keys: cin = t2i_data['conv_in.weight'].shape[1] channel = t2i_data['conv_in.weight'].shape[0] ksize = t2i_data['body.0.block2.weight'].shape[2] use_conv = False down_opts = list(filter(lambda a: a.endswith("down_opt.op.weight"), keys)) if len(down_opts) > 0: use_conv = True model_ad = adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv) else: return None model_ad.load_state_dict(t2i_data) return T2IAdapter(model_ad, cin // 64) class StyleModel: def __init__(self, model, device="cpu"): self.model = model def get_cond(self, input): return self.model(input.last_hidden_state) def load_style_model(ckpt_path): model_data = utils.load_torch_file(ckpt_path, safe_load=True) keys = model_data.keys() if "style_embedding" in keys: model = adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8) else: raise Exception("invalid style model {}".format(ckpt_path)) model.load_state_dict(model_data) return StyleModel(model) def load_clip(ckpt_paths, embedding_directory=None): clip_data = [] for p in ckpt_paths: clip_data.append(utils.load_torch_file(p, safe_load=True)) class EmptyClass: pass for i in range(len(clip_data)): if "transformer.resblocks.0.ln_1.weight" in clip_data[i]: clip_data[i] = utils.transformers_convert(clip_data[i], "", "text_model.", 32) clip_target = EmptyClass() clip_target.params = {} if len(clip_data) == 1: if "text_model.encoder.layers.30.mlp.fc1.weight" in clip_data[0]: clip_target.clip = sdxl_clip.SDXLRefinerClipModel clip_target.tokenizer = sdxl_clip.SDXLTokenizer elif "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data[0]: clip_target.clip = sd2_clip.SD2ClipModel clip_target.tokenizer = sd2_clip.SD2Tokenizer else: clip_target.clip = sd1_clip.SD1ClipModel clip_target.tokenizer = sd1_clip.SD1Tokenizer else: clip_target.clip = sdxl_clip.SDXLClipModel clip_target.tokenizer = sdxl_clip.SDXLTokenizer clip = CLIP(clip_target, embedding_directory=embedding_directory) for c in clip_data: m, u = clip.load_sd(c) if len(m) > 0: print("clip missing:", m) if len(u) > 0: print("clip unexpected:", u) return clip def load_gligen(ckpt_path): data = utils.load_torch_file(ckpt_path, safe_load=True) model = gligen.load_gligen(data) if model_management.should_use_fp16(): model = model.half() return model 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 if config is None: with open(config_path, 'r') as stream: config = yaml.safe_load(stream) model_config_params = config['model']['params'] clip_config = model_config_params['cond_stage_config'] scale_factor = model_config_params['scale_factor'] vae_config = model_config_params['first_stage_config'] fp16 = False if "unet_config" in model_config_params: if "params" in model_config_params["unet_config"]: unet_config = model_config_params["unet_config"]["params"] if "use_fp16" in unet_config: fp16 = unet_config["use_fp16"] noise_aug_config = None if "noise_aug_config" in model_config_params: noise_aug_config = model_config_params["noise_aug_config"] v_prediction = False if "parameterization" in model_config_params: if model_config_params["parameterization"] == "v": v_prediction = True clip = None vae = None class WeightsLoader(torch.nn.Module): pass if state_dict is None: state_dict = utils.load_torch_file(ckpt_path) class EmptyClass: pass model_config = EmptyClass() model_config.unet_config = unet_config from . import latent_formats model_config.latent_format = latent_formats.SD15(scale_factor=scale_factor) if config['model']["target"].endswith("LatentInpaintDiffusion"): model = model_base.SDInpaint(model_config, v_prediction=v_prediction) elif config['model']["target"].endswith("ImageEmbeddingConditionedLatentDiffusion"): model = model_base.SD21UNCLIP(model_config, noise_aug_config["params"], v_prediction=v_prediction) else: model = model_base.BaseModel(model_config, v_prediction=v_prediction) if fp16: model = model.half() offload_device = model_management.unet_offload_device() model = model.to(offload_device) model.load_model_weights(state_dict, "model.diffusion_model.") if output_vae: w = WeightsLoader() vae = VAE(config=vae_config) w.first_stage_model = vae.first_stage_model load_model_weights(w, state_dict) if output_clip: w = WeightsLoader() clip_target = EmptyClass() clip_target.params = clip_config.get("params", {}) if clip_config["target"].endswith("FrozenOpenCLIPEmbedder"): clip_target.clip = sd2_clip.SD2ClipModel clip_target.tokenizer = sd2_clip.SD2Tokenizer elif clip_config["target"].endswith("FrozenCLIPEmbedder"): clip_target.clip = sd1_clip.SD1ClipModel clip_target.tokenizer = sd1_clip.SD1Tokenizer clip = CLIP(clip_target, embedding_directory=embedding_directory) w.cond_stage_model = clip.cond_stage_model load_clip_weights(w, state_dict) return (ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae) def calculate_parameters(sd, prefix): params = 0 for k in sd.keys(): if k.startswith(prefix): params += sd[k].nelement() return params def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None): sd = utils.load_torch_file(ckpt_path) sd_keys = sd.keys() clip = None clipvision = None vae = None model = None clip_target = None parameters = calculate_parameters(sd, "model.diffusion_model.") fp16 = model_management.should_use_fp16(model_params=parameters) class WeightsLoader(torch.nn.Module): pass model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.", fp16) if model_config is None: raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path)) if model_config.clip_vision_prefix is not None: if output_clipvision: clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True) offload_device = model_management.unet_offload_device() model = model_config.get_model(sd, "model.diffusion_model.") model = model.to(offload_device) model.load_model_weights(sd, "model.diffusion_model.") if output_vae: vae = VAE() w = WeightsLoader() w.first_stage_model = vae.first_stage_model load_model_weights(w, sd) if output_clip: w = WeightsLoader() clip_target = model_config.clip_target() clip = CLIP(clip_target, embedding_directory=embedding_directory) w.cond_stage_model = clip.cond_stage_model sd = model_config.process_clip_state_dict(sd) load_model_weights(w, sd) left_over = sd.keys() 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) def load_unet(unet_path): #load unet in diffusers format sd = utils.load_torch_file(unet_path) parameters = calculate_parameters(sd, "") fp16 = model_management.should_use_fp16(model_params=parameters) match = {} match["context_dim"] = sd["down_blocks.0.attentions.1.transformer_blocks.0.attn2.to_k.weight"].shape[1] match["model_channels"] = sd["conv_in.weight"].shape[0] match["in_channels"] = sd["conv_in.weight"].shape[1] match["adm_in_channels"] = None if "class_embedding.linear_1.weight" in sd: match["adm_in_channels"] = sd["class_embedding.linear_1.weight"].shape[1] 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': 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} 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': 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} SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, 'use_fp16': 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} 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': True, '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} 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': True, '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} SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, 'use_fp16': True, '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} supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl] print("match", match) for unet_config in supported_models: matches = True for k in match: if match[k] != unet_config[k]: matches = False break if matches: diffusers_keys = utils.unet_to_diffusers(unet_config) new_sd = {} for k in diffusers_keys: if k in sd: new_sd[diffusers_keys[k]] = sd.pop(k) else: print(diffusers_keys[k], k) offload_device = model_management.unet_offload_device() model_config = model_detection.model_config_from_unet_config(unet_config) model = model_config.get_model(new_sd, "") model = model.to(offload_device) model.load_model_weights(new_sd, "") 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