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| import torch | |
| from ldm_patched.modules import model_management | |
| from ldm_patched.ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine | |
| import yaml | |
| import ldm_patched.modules.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 | |
| import ldm_patched.modules.model_patcher | |
| import ldm_patched.modules.lora | |
| import ldm_patched.t2ia.adapter | |
| import ldm_patched.modules.supported_models_base | |
| import ldm_patched.taesd.taesd | |
| 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("extra", 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 = ldm_patched.modules.utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24) | |
| return load_model_weights(model, sd) | |
| def load_lora_for_models(model, clip, lora, strength_model, strength_clip): | |
| key_map = {} | |
| if model is not None: | |
| key_map = ldm_patched.modules.lora.model_lora_keys_unet(model.model, key_map) | |
| if clip is not None: | |
| key_map = ldm_patched.modules.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) | |
| loaded = ldm_patched.modules.lora.load_lora(lora, key_map) | |
| if model is not None: | |
| new_modelpatcher = model.clone() | |
| k = new_modelpatcher.add_patches(loaded, strength_model) | |
| else: | |
| k = () | |
| new_modelpatcher = None | |
| if clip is not None: | |
| new_clip = clip.clone() | |
| k1 = new_clip.add_patches(loaded, strength_clip) | |
| else: | |
| k1 = () | |
| new_clip = None | |
| 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'] = offload_device | |
| params['dtype'] = model_management.text_encoder_dtype(load_device) | |
| self.cond_stage_model = clip(**(params)) | |
| self.tokenizer = tokenizer(embedding_directory=embedding_directory) | |
| self.patcher = ldm_patched.modules.model_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 add_patches(self, patches, strength_patch=1.0, strength_model=1.0): | |
| return self.patcher.add_patches(patches, strength_patch, strength_model) | |
| 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) | |
| else: | |
| self.cond_stage_model.reset_clip_layer() | |
| self.load_model() | |
| 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 load_model(self): | |
| model_management.load_model_gpu(self.patcher) | |
| return self.patcher | |
| def get_key_patches(self): | |
| return self.patcher.get_key_patches() | |
| class VAE: | |
| def __init__(self, sd=None, device=None, config=None, dtype=None): | |
| if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format | |
| sd = diffusers_convert.convert_vae_state_dict(sd) | |
| self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) #These are for AutoencoderKL and need tweaking (should be lower) | |
| self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) | |
| self.downscale_ratio = 8 | |
| self.latent_channels = 4 | |
| if config is None: | |
| if "decoder.mid.block_1.mix_factor" in sd: | |
| encoder_config = {'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} | |
| decoder_config = encoder_config.copy() | |
| decoder_config["video_kernel_size"] = [3, 1, 1] | |
| decoder_config["alpha"] = 0.0 | |
| self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "ldm_patched.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, | |
| encoder_config={'target': "ldm_patched.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config}, | |
| decoder_config={'target': "ldm_patched.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config}) | |
| elif "taesd_decoder.1.weight" in sd: | |
| self.first_stage_model = ldm_patched.taesd.taesd.TAESD() | |
| else: | |
| #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} | |
| if 'encoder.down.2.downsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE | |
| ddconfig['ch_mult'] = [1, 2, 4] | |
| self.downscale_ratio = 4 | |
| self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=4) | |
| else: | |
| self.first_stage_model = AutoencoderKL(**(config['params'])) | |
| self.first_stage_model = self.first_stage_model.eval() | |
| m, u = self.first_stage_model.load_state_dict(sd, strict=False) | |
| if len(m) > 0: | |
| print("Missing VAE keys", m) | |
| if len(u) > 0: | |
| print("Leftover VAE keys", u) | |
| if device is None: | |
| device = model_management.vae_device() | |
| self.device = device | |
| offload_device = model_management.vae_offload_device() | |
| if dtype is None: | |
| dtype = model_management.vae_dtype() | |
| self.vae_dtype = dtype | |
| self.first_stage_model.to(self.vae_dtype) | |
| self.output_device = model_management.intermediate_device() | |
| self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device) | |
| def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16): | |
| steps = samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap) | |
| steps += samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap) | |
| steps += samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap) | |
| pbar = ldm_patched.modules.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(( | |
| (ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar) + | |
| ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar) + | |
| ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, 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] * ldm_patched.modules.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) | |
| steps += pixel_samples.shape[0] * ldm_patched.modules.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] * ldm_patched.modules.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap) | |
| pbar = ldm_patched.modules.utils.ProgressBar(steps) | |
| encode_fn = lambda a: self.first_stage_model.encode((2. * a - 1.).to(self.vae_dtype).to(self.device)).float() | |
| samples = ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) | |
| samples += ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) | |
| samples += ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) | |
| samples /= 3.0 | |
| return samples | |
| def decode(self, samples_in): | |
| try: | |
| memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype) | |
| model_management.load_models_gpu([self.patcher], memory_required=memory_used) | |
| free_memory = model_management.get_free_memory(self.device) | |
| 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] * self.downscale_ratio), round(samples_in.shape[3] * self.downscale_ratio)), device=self.output_device) | |
| 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).to(self.output_device).float() + 1.0) / 2.0, min=0.0, max=1.0) | |
| 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) | |
| pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1) | |
| return pixel_samples | |
| def decode_tiled(self, samples, tile_x=64, tile_y=64, overlap = 16): | |
| model_management.load_model_gpu(self.patcher) | |
| output = self.decode_tiled_(samples, tile_x, tile_y, overlap) | |
| return output.movedim(1,-1) | |
| def encode(self, pixel_samples): | |
| pixel_samples = pixel_samples.movedim(-1,1) | |
| try: | |
| memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype) | |
| model_management.load_models_gpu([self.patcher], memory_required=memory_used) | |
| free_memory = model_management.get_free_memory(self.device) | |
| batch_number = int(free_memory / memory_used) | |
| batch_number = max(1, batch_number) | |
| samples = torch.empty((pixel_samples.shape[0], self.latent_channels, round(pixel_samples.shape[2] // self.downscale_ratio), round(pixel_samples.shape[3] // self.downscale_ratio)), device=self.output_device) | |
| 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).to(self.output_device).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) | |
| return samples | |
| def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): | |
| model_management.load_model_gpu(self.patcher) | |
| pixel_samples = pixel_samples.movedim(-1,1) | |
| samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap) | |
| return samples | |
| def get_sd(self): | |
| return self.first_stage_model.state_dict() | |
| 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 = ldm_patched.modules.utils.load_torch_file(ckpt_path, safe_load=True) | |
| keys = model_data.keys() | |
| if "style_embedding" in keys: | |
| model = ldm_patched.t2ia.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(ldm_patched.modules.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] = ldm_patched.modules.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 = ldm_patched.modules.utils.load_torch_file(ckpt_path, safe_load=True) | |
| model = gligen.load_gligen(data) | |
| if model_management.should_use_fp16(): | |
| model = model.half() | |
| return ldm_patched.modules.model_patcher.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 | |
| 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.pop("use_fp16") | |
| if fp16: | |
| unet_config["dtype"] = torch.float16 | |
| noise_aug_config = None | |
| if "noise_aug_config" in model_config_params: | |
| noise_aug_config = model_config_params["noise_aug_config"] | |
| model_type = model_base.ModelType.EPS | |
| if "parameterization" in model_config_params: | |
| if model_config_params["parameterization"] == "v": | |
| model_type = model_base.ModelType.V_PREDICTION | |
| clip = None | |
| vae = None | |
| class WeightsLoader(torch.nn.Module): | |
| pass | |
| if state_dict is None: | |
| state_dict = ldm_patched.modules.utils.load_torch_file(ckpt_path) | |
| class EmptyClass: | |
| pass | |
| model_config = ldm_patched.modules.supported_models_base.BASE({}) | |
| from . import latent_formats | |
| model_config.latent_format = latent_formats.SD15(scale_factor=scale_factor) | |
| model_config.unet_config = model_detection.convert_config(unet_config) | |
| if config['model']["target"].endswith("ImageEmbeddingConditionedLatentDiffusion"): | |
| model = model_base.SD21UNCLIP(model_config, noise_aug_config["params"], model_type=model_type) | |
| else: | |
| model = model_base.BaseModel(model_config, model_type=model_type) | |
| if config['model']["target"].endswith("LatentInpaintDiffusion"): | |
| model.set_inpaint() | |
| 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: | |
| vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(state_dict, {"first_stage_model.": ""}, filter_keys=True) | |
| vae = VAE(sd=vae_sd, config=vae_config) | |
| 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 | |
| clip = CLIP(clip_target, embedding_directory=embedding_directory) | |
| w.cond_stage_model = clip.cond_stage_model.clip_h | |
| 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.clip_l | |
| load_clip_weights(w, state_dict) | |
| return (ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae) | |
| def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True): | |
| sd = ldm_patched.modules.utils.load_torch_file(ckpt_path) | |
| sd_keys = sd.keys() | |
| clip = None | |
| clipvision = None | |
| vae = None | |
| model = None | |
| model_patcher = None | |
| clip_target = None | |
| parameters = ldm_patched.modules.utils.calculate_parameters(sd, "model.diffusion_model.") | |
| unet_dtype = model_management.unet_dtype(model_params=parameters) | |
| load_device = model_management.get_torch_device() | |
| manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device) | |
| class WeightsLoader(torch.nn.Module): | |
| pass | |
| model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.", unet_dtype) | |
| model_config.set_manual_cast(manual_cast_dtype) | |
| 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) | |
| if output_model: | |
| inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype) | |
| offload_device = model_management.unet_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: | |
| vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"first_stage_model.": ""}, filter_keys=True) | |
| vae_sd = model_config.process_vae_state_dict(vae_sd) | |
| vae = VAE(sd=vae_sd) | |
| if output_clip: | |
| w = WeightsLoader() | |
| clip_target = model_config.clip_target() | |
| if clip_target is not None: | |
| 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) | |
| if output_model: | |
| model_patcher = ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=load_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_state_dict(sd): #load unet in diffusers format | |
| parameters = ldm_patched.modules.utils.calculate_parameters(sd) | |
| unet_dtype = model_management.unet_dtype(model_params=parameters) | |
| load_device = model_management.get_torch_device() | |
| manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device) | |
| if "input_blocks.0.0.weight" in sd: #ldm | |
| model_config = model_detection.model_config_from_unet(sd, "", unet_dtype) | |
| if model_config is None: | |
| return None | |
| new_sd = sd | |
| else: #diffusers | |
| model_config = model_detection.model_config_from_diffusers_unet(sd, unet_dtype) | |
| if model_config is None: | |
| return None | |
| diffusers_keys = ldm_patched.modules.utils.unet_to_diffusers(model_config.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.set_manual_cast(manual_cast_dtype) | |
| model = model_config.get_model(new_sd, "") | |
| model = model.to(offload_device) | |
| model.load_model_weights(new_sd, "") | |
| left_over = sd.keys() | |
| if len(left_over) > 0: | |
| print("left over keys in unet:", left_over) | |
| return ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device) | |
| def load_unet(unet_path): | |
| sd = ldm_patched.modules.utils.load_torch_file(unet_path) | |
| model = load_unet_state_dict(sd) | |
| if model is None: | |
| print("ERROR UNSUPPORTED UNET", unet_path) | |
| raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path)) | |
| return model | |
| def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None): | |
| clip_sd = None | |
| load_models = [model] | |
| if clip is not None: | |
| load_models.append(clip.load_model()) | |
| clip_sd = clip.get_sd() | |
| model_management.load_models_gpu(load_models) | |
| clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None | |
| sd = model.model.state_dict_for_saving(clip_sd, vae.get_sd(), clip_vision_sd) | |
| ldm_patched.modules.utils.save_torch_file(sd, output_path, metadata=metadata) | |