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| import torch | |
| from enum import Enum | |
| import logging | |
| from comfy import model_management | |
| from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine | |
| from .ldm.cascade.stage_a import StageA | |
| from .ldm.cascade.stage_c_coder import StageC_coder | |
| from .ldm.audio.autoencoder import AudioOobleckVAE | |
| import comfy.ldm.genmo.vae.model | |
| import comfy.ldm.lightricks.vae.causal_video_autoencoder | |
| import yaml | |
| import comfy.utils | |
| from . import clip_vision | |
| from . import gligen | |
| from . import diffusers_convert | |
| from . import model_detection | |
| from . import sd1_clip | |
| from . import sdxl_clip | |
| import comfy.text_encoders.sd2_clip | |
| import comfy.text_encoders.sd3_clip | |
| import comfy.text_encoders.sa_t5 | |
| import comfy.text_encoders.aura_t5 | |
| import comfy.text_encoders.hydit | |
| import comfy.text_encoders.flux | |
| import comfy.text_encoders.long_clipl | |
| import comfy.text_encoders.genmo | |
| import comfy.text_encoders.lt | |
| import comfy.model_patcher | |
| import comfy.lora | |
| import comfy.lora_convert | |
| import comfy.t2i_adapter.adapter | |
| import comfy.taesd.taesd | |
| import comfy.ldm.flux.redux | |
| def load_lora_for_models(model, clip, lora, strength_model, strength_clip): | |
| key_map = {} | |
| if model is not None: | |
| key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) | |
| if clip is not None: | |
| key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) | |
| lora = comfy.lora_convert.convert_lora(lora) | |
| loaded = comfy.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): | |
| logging.warning("NOT LOADED {}".format(x)) | |
| return (new_modelpatcher, new_clip) | |
| class CLIP: | |
| def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}, parameters=0, model_options={}): | |
| if no_init: | |
| return | |
| params = target.params.copy() | |
| clip = target.clip | |
| tokenizer = target.tokenizer | |
| load_device = model_options.get("load_device", model_management.text_encoder_device()) | |
| offload_device = model_options.get("offload_device", model_management.text_encoder_offload_device()) | |
| dtype = model_options.get("dtype", None) | |
| if dtype is None: | |
| dtype = model_management.text_encoder_dtype(load_device) | |
| params['dtype'] = dtype | |
| params['device'] = model_options.get("initial_device", model_management.text_encoder_initial_device(load_device, offload_device, parameters * model_management.dtype_size(dtype))) | |
| params['model_options'] = model_options | |
| self.cond_stage_model = clip(**(params)) | |
| for dt in self.cond_stage_model.dtypes: | |
| if not model_management.supports_cast(load_device, dt): | |
| load_device = offload_device | |
| if params['device'] != offload_device: | |
| self.cond_stage_model.to(offload_device) | |
| logging.warning("Had to shift TE back.") | |
| self.tokenizer = tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) | |
| self.patcher = comfy.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device) | |
| if params['device'] == load_device: | |
| model_management.load_models_gpu([self.patcher], force_full_load=True) | |
| self.layer_idx = None | |
| logging.debug("CLIP model load device: {}, offload device: {}, current: {}".format(load_device, offload_device, params['device'])) | |
| 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, return_dict=False): | |
| self.cond_stage_model.reset_clip_options() | |
| if self.layer_idx is not None: | |
| self.cond_stage_model.set_clip_options({"layer": self.layer_idx}) | |
| if return_pooled == "unprojected": | |
| self.cond_stage_model.set_clip_options({"projected_pooled": False}) | |
| self.load_model() | |
| o = self.cond_stage_model.encode_token_weights(tokens) | |
| cond, pooled = o[:2] | |
| if return_dict: | |
| out = {"cond": cond, "pooled_output": pooled} | |
| if len(o) > 2: | |
| for k in o[2]: | |
| out[k] = o[2][k] | |
| return out | |
| 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, full_model=False): | |
| if full_model: | |
| return self.cond_stage_model.load_state_dict(sd, strict=False) | |
| else: | |
| return self.cond_stage_model.load_sd(sd) | |
| def get_sd(self): | |
| sd_clip = self.cond_stage_model.state_dict() | |
| sd_tokenizer = self.tokenizer.state_dict() | |
| for k in sd_tokenizer: | |
| sd_clip[k] = sd_tokenizer[k] | |
| return sd_clip | |
| 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.upscale_ratio = 8 | |
| self.latent_channels = 4 | |
| self.latent_dim = 2 | |
| self.output_channels = 3 | |
| self.process_input = lambda image: image * 2.0 - 1.0 | |
| self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0) | |
| self.working_dtypes = [torch.bfloat16, torch.float32] | |
| 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': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, | |
| encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config}, | |
| decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config}) | |
| elif "taesd_decoder.1.weight" in sd: | |
| self.latent_channels = sd["taesd_decoder.1.weight"].shape[1] | |
| self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels) | |
| elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade | |
| self.first_stage_model = StageA() | |
| self.downscale_ratio = 4 | |
| self.upscale_ratio = 4 | |
| #TODO | |
| #self.memory_used_encode | |
| #self.memory_used_decode | |
| self.process_input = lambda image: image | |
| self.process_output = lambda image: image | |
| elif "backbone.1.0.block.0.1.num_batches_tracked" in sd: #effnet: encoder for stage c latent of stable cascade | |
| self.first_stage_model = StageC_coder() | |
| self.downscale_ratio = 32 | |
| self.latent_channels = 16 | |
| new_sd = {} | |
| for k in sd: | |
| new_sd["encoder.{}".format(k)] = sd[k] | |
| sd = new_sd | |
| elif "blocks.11.num_batches_tracked" in sd: #previewer: decoder for stage c latent of stable cascade | |
| self.first_stage_model = StageC_coder() | |
| self.latent_channels = 16 | |
| new_sd = {} | |
| for k in sd: | |
| new_sd["previewer.{}".format(k)] = sd[k] | |
| sd = new_sd | |
| elif "encoder.backbone.1.0.block.0.1.num_batches_tracked" in sd: #combined effnet and previewer for stable cascade | |
| self.first_stage_model = StageC_coder() | |
| self.downscale_ratio = 32 | |
| self.latent_channels = 16 | |
| elif "decoder.conv_in.weight" in sd: | |
| #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 and 'decoder.up.3.upsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE | |
| ddconfig['ch_mult'] = [1, 2, 4] | |
| self.downscale_ratio = 4 | |
| self.upscale_ratio = 4 | |
| self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1] | |
| if 'quant_conv.weight' in sd: | |
| self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=4) | |
| else: | |
| self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, | |
| encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig}, | |
| decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig}) | |
| elif "decoder.layers.1.layers.0.beta" in sd: | |
| self.first_stage_model = AudioOobleckVAE() | |
| self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype) | |
| self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * 2048) * model_management.dtype_size(dtype) | |
| self.latent_channels = 64 | |
| self.output_channels = 2 | |
| self.upscale_ratio = 2048 | |
| self.downscale_ratio = 2048 | |
| self.latent_dim = 1 | |
| self.process_output = lambda audio: audio | |
| self.process_input = lambda audio: audio | |
| self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] | |
| elif "blocks.2.blocks.3.stack.5.weight" in sd or "decoder.blocks.2.blocks.3.stack.5.weight" in sd or "layers.4.layers.1.attn_block.attn.qkv.weight" in sd or "encoder.layers.4.layers.1.attn_block.attn.qkv.weight" in sd: #genmo mochi vae | |
| if "blocks.2.blocks.3.stack.5.weight" in sd: | |
| sd = comfy.utils.state_dict_prefix_replace(sd, {"": "decoder."}) | |
| if "layers.4.layers.1.attn_block.attn.qkv.weight" in sd: | |
| sd = comfy.utils.state_dict_prefix_replace(sd, {"": "encoder."}) | |
| self.first_stage_model = comfy.ldm.genmo.vae.model.VideoVAE() | |
| self.latent_channels = 12 | |
| self.latent_dim = 3 | |
| self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype) | |
| self.memory_used_encode = lambda shape, dtype: (1.5 * max(shape[2], 7) * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype) | |
| self.upscale_ratio = (lambda a: max(0, a * 6 - 5), 8, 8) | |
| self.working_dtypes = [torch.float16, torch.float32] | |
| elif "decoder.up_blocks.0.res_blocks.0.conv1.conv.weight" in sd: #lightricks ltxv | |
| self.first_stage_model = comfy.ldm.lightricks.vae.causal_video_autoencoder.VideoVAE() | |
| self.latent_channels = 128 | |
| self.latent_dim = 3 | |
| self.memory_used_decode = lambda shape, dtype: (900 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype) | |
| self.memory_used_encode = lambda shape, dtype: (70 * max(shape[2], 7) * shape[3] * shape[4]) * model_management.dtype_size(dtype) | |
| self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 32, 32) | |
| self.working_dtypes = [torch.bfloat16, torch.float32] | |
| else: | |
| logging.warning("WARNING: No VAE weights detected, VAE not initalized.") | |
| self.first_stage_model = None | |
| return | |
| 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: | |
| logging.warning("Missing VAE keys {}".format(m)) | |
| if len(u) > 0: | |
| logging.debug("Leftover VAE keys {}".format(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.device, self.working_dtypes) | |
| self.vae_dtype = dtype | |
| self.first_stage_model.to(self.vae_dtype) | |
| self.output_device = model_management.intermediate_device() | |
| self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device) | |
| logging.debug("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype)) | |
| def vae_encode_crop_pixels(self, pixels): | |
| dims = pixels.shape[1:-1] | |
| for d in range(len(dims)): | |
| x = (dims[d] // self.downscale_ratio) * self.downscale_ratio | |
| x_offset = (dims[d] % self.downscale_ratio) // 2 | |
| if x != dims[d]: | |
| pixels = pixels.narrow(d + 1, x_offset, x) | |
| return pixels | |
| def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16): | |
| steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap) | |
| steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap) | |
| steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap) | |
| pbar = comfy.utils.ProgressBar(steps) | |
| decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() | |
| output = self.process_output( | |
| (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) + | |
| comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) + | |
| comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar)) | |
| / 3.0) | |
| return output | |
| def decode_tiled_1d(self, samples, tile_x=128, overlap=32): | |
| decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() | |
| return comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device) | |
| def decode_tiled_3d(self, samples, tile_t=999, tile_x=32, tile_y=32, overlap=(1, 8, 8)): | |
| decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() | |
| return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device)) | |
| def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): | |
| steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) | |
| steps += pixel_samples.shape[0] * comfy.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] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap) | |
| pbar = comfy.utils.ProgressBar(steps) | |
| encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() | |
| samples = comfy.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 += comfy.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 += comfy.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 encode_tiled_1d(self, samples, tile_x=128 * 2048, overlap=32 * 2048): | |
| encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() | |
| return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=(1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device) | |
| def decode(self, samples_in): | |
| pixel_samples = None | |
| 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) | |
| 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) | |
| out = self.process_output(self.first_stage_model.decode(samples).to(self.output_device).float()) | |
| if pixel_samples is None: | |
| pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device) | |
| pixel_samples[x:x+batch_number] = out | |
| except model_management.OOM_EXCEPTION as e: | |
| logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.") | |
| dims = samples_in.ndim - 2 | |
| if dims == 1: | |
| pixel_samples = self.decode_tiled_1d(samples_in) | |
| elif dims == 2: | |
| pixel_samples = self.decode_tiled_(samples_in) | |
| elif dims == 3: | |
| tile = 256 // self.spacial_compression_decode() | |
| overlap = tile // 4 | |
| pixel_samples = self.decode_tiled_3d(samples_in, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap)) | |
| pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1) | |
| return pixel_samples | |
| def decode_tiled(self, samples, tile_x=None, tile_y=None, overlap=None): | |
| memory_used = self.memory_used_decode(samples.shape, self.vae_dtype) #TODO: calculate mem required for tile | |
| model_management.load_models_gpu([self.patcher], memory_required=memory_used) | |
| dims = samples.ndim - 2 | |
| args = {} | |
| if tile_x is not None: | |
| args["tile_x"] = tile_x | |
| if tile_y is not None: | |
| args["tile_y"] = tile_y | |
| if overlap is not None: | |
| args["overlap"] = overlap | |
| if dims == 1: | |
| args.pop("tile_y") | |
| output = self.decode_tiled_1d(samples, **args) | |
| elif dims == 2: | |
| output = self.decode_tiled_(samples, **args) | |
| elif dims == 3: | |
| output = self.decode_tiled_3d(samples, **args) | |
| return output.movedim(1, -1) | |
| def encode(self, pixel_samples): | |
| pixel_samples = self.vae_encode_crop_pixels(pixel_samples) | |
| pixel_samples = pixel_samples.movedim(-1, 1) | |
| if self.latent_dim == 3: | |
| pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0) | |
| 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 / max(1, memory_used)) | |
| batch_number = max(1, batch_number) | |
| samples = None | |
| for x in range(0, pixel_samples.shape[0], batch_number): | |
| pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype).to(self.device) | |
| out = self.first_stage_model.encode(pixels_in).to(self.output_device).float() | |
| if samples is None: | |
| samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device) | |
| samples[x:x + batch_number] = out | |
| except model_management.OOM_EXCEPTION as e: | |
| logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.") | |
| if len(pixel_samples.shape) == 3: | |
| samples = self.encode_tiled_1d(pixel_samples) | |
| else: | |
| samples = self.encode_tiled_(pixel_samples) | |
| return samples | |
| def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): | |
| pixel_samples = self.vae_encode_crop_pixels(pixel_samples) | |
| 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() | |
| def spacial_compression_decode(self): | |
| try: | |
| return self.upscale_ratio[-1] | |
| except: | |
| return self.upscale_ratio | |
| 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 = comfy.utils.load_torch_file(ckpt_path, safe_load=True) | |
| keys = model_data.keys() | |
| if "style_embedding" in keys: | |
| model = comfy.t2i_adapter.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8) | |
| elif "redux_down.weight" in keys: | |
| model = comfy.ldm.flux.redux.ReduxImageEncoder() | |
| else: | |
| raise Exception("invalid style model {}".format(ckpt_path)) | |
| model.load_state_dict(model_data) | |
| return StyleModel(model) | |
| class CLIPType(Enum): | |
| STABLE_DIFFUSION = 1 | |
| STABLE_CASCADE = 2 | |
| SD3 = 3 | |
| STABLE_AUDIO = 4 | |
| HUNYUAN_DIT = 5 | |
| FLUX = 6 | |
| MOCHI = 7 | |
| LTXV = 8 | |
| def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}): | |
| clip_data = [] | |
| for p in ckpt_paths: | |
| clip_data.append(comfy.utils.load_torch_file(p, safe_load=True)) | |
| return load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options) | |
| class TEModel(Enum): | |
| CLIP_L = 1 | |
| CLIP_H = 2 | |
| CLIP_G = 3 | |
| T5_XXL = 4 | |
| T5_XL = 5 | |
| T5_BASE = 6 | |
| def detect_te_model(sd): | |
| if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: | |
| return TEModel.CLIP_G | |
| if "text_model.encoder.layers.22.mlp.fc1.weight" in sd: | |
| return TEModel.CLIP_H | |
| if "text_model.encoder.layers.0.mlp.fc1.weight" in sd: | |
| return TEModel.CLIP_L | |
| if "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in sd: | |
| weight = sd["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"] | |
| if weight.shape[-1] == 4096: | |
| return TEModel.T5_XXL | |
| elif weight.shape[-1] == 2048: | |
| return TEModel.T5_XL | |
| if "encoder.block.0.layer.0.SelfAttention.k.weight" in sd: | |
| return TEModel.T5_BASE | |
| return None | |
| def t5xxl_detect(clip_data): | |
| weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" | |
| for sd in clip_data: | |
| if weight_name in sd: | |
| return comfy.text_encoders.sd3_clip.t5_xxl_detect(sd) | |
| return {} | |
| def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}): | |
| clip_data = state_dicts | |
| class EmptyClass: | |
| pass | |
| for i in range(len(clip_data)): | |
| if "transformer.resblocks.0.ln_1.weight" in clip_data[i]: | |
| clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "") | |
| else: | |
| if "text_projection" in clip_data[i]: | |
| clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node | |
| clip_target = EmptyClass() | |
| clip_target.params = {} | |
| if len(clip_data) == 1: | |
| te_model = detect_te_model(clip_data[0]) | |
| if te_model == TEModel.CLIP_G: | |
| if clip_type == CLIPType.STABLE_CASCADE: | |
| clip_target.clip = sdxl_clip.StableCascadeClipModel | |
| clip_target.tokenizer = sdxl_clip.StableCascadeTokenizer | |
| elif clip_type == CLIPType.SD3: | |
| clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=True, t5=False) | |
| clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer | |
| else: | |
| clip_target.clip = sdxl_clip.SDXLRefinerClipModel | |
| clip_target.tokenizer = sdxl_clip.SDXLTokenizer | |
| elif te_model == TEModel.CLIP_H: | |
| clip_target.clip = comfy.text_encoders.sd2_clip.SD2ClipModel | |
| clip_target.tokenizer = comfy.text_encoders.sd2_clip.SD2Tokenizer | |
| elif te_model == TEModel.T5_XXL: | |
| if clip_type == CLIPType.SD3: | |
| clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, **t5xxl_detect(clip_data)) | |
| clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer | |
| elif clip_type == CLIPType.LTXV: | |
| clip_target.clip = comfy.text_encoders.lt.ltxv_te(**t5xxl_detect(clip_data)) | |
| clip_target.tokenizer = comfy.text_encoders.lt.LTXVT5Tokenizer | |
| else: #CLIPType.MOCHI | |
| clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data)) | |
| clip_target.tokenizer = comfy.text_encoders.genmo.MochiT5Tokenizer | |
| elif te_model == TEModel.T5_XL: | |
| clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model | |
| clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer | |
| elif te_model == TEModel.T5_BASE: | |
| clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model | |
| clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer | |
| else: | |
| if clip_type == CLIPType.SD3: | |
| clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False) | |
| clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer | |
| else: | |
| clip_target.clip = sd1_clip.SD1ClipModel | |
| clip_target.tokenizer = sd1_clip.SD1Tokenizer | |
| elif len(clip_data) == 2: | |
| if clip_type == CLIPType.SD3: | |
| te_models = [detect_te_model(clip_data[0]), detect_te_model(clip_data[1])] | |
| clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=TEModel.CLIP_L in te_models, clip_g=TEModel.CLIP_G in te_models, t5=TEModel.T5_XXL in te_models, **t5xxl_detect(clip_data)) | |
| clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer | |
| elif clip_type == CLIPType.HUNYUAN_DIT: | |
| clip_target.clip = comfy.text_encoders.hydit.HyditModel | |
| clip_target.tokenizer = comfy.text_encoders.hydit.HyditTokenizer | |
| elif clip_type == CLIPType.FLUX: | |
| clip_target.clip = comfy.text_encoders.flux.flux_clip(**t5xxl_detect(clip_data)) | |
| clip_target.tokenizer = comfy.text_encoders.flux.FluxTokenizer | |
| else: | |
| clip_target.clip = sdxl_clip.SDXLClipModel | |
| clip_target.tokenizer = sdxl_clip.SDXLTokenizer | |
| elif len(clip_data) == 3: | |
| clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(**t5xxl_detect(clip_data)) | |
| clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer | |
| parameters = 0 | |
| tokenizer_data = {} | |
| for c in clip_data: | |
| parameters += comfy.utils.calculate_parameters(c) | |
| tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options) | |
| clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, model_options=model_options) | |
| for c in clip_data: | |
| m, u = clip.load_sd(c) | |
| if len(m) > 0: | |
| logging.warning("clip missing: {}".format(m)) | |
| if len(u) > 0: | |
| logging.debug("clip unexpected: {}".format(u)) | |
| return clip | |
| def load_gligen(ckpt_path): | |
| data = comfy.utils.load_torch_file(ckpt_path, safe_load=True) | |
| model = gligen.load_gligen(data) | |
| if model_management.should_use_fp16(): | |
| model = model.half() | |
| return comfy.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): | |
| logging.warning("Warning: The load checkpoint with config function is deprecated and will eventually be removed, please use the other one.") | |
| model, clip, vae, _ = load_checkpoint_guess_config(ckpt_path, output_vae=output_vae, output_clip=output_clip, output_clipvision=False, embedding_directory=embedding_directory, output_model=True) | |
| #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'] | |
| if "parameterization" in model_config_params: | |
| if model_config_params["parameterization"] == "v": | |
| m = model.clone() | |
| class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingDiscrete, comfy.model_sampling.V_PREDICTION): | |
| pass | |
| m.add_object_patch("model_sampling", ModelSamplingAdvanced(model.model.model_config)) | |
| model = m | |
| layer_idx = clip_config.get("params", {}).get("layer_idx", None) | |
| if layer_idx is not None: | |
| clip.clip_layer(layer_idx) | |
| return (model, clip, vae) | |
| def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}): | |
| sd = comfy.utils.load_torch_file(ckpt_path) | |
| out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options) | |
| if out is None: | |
| raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path)) | |
| return out | |
| def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}): | |
| clip = None | |
| clipvision = None | |
| vae = None | |
| model = None | |
| model_patcher = None | |
| diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd) | |
| parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix) | |
| weight_dtype = comfy.utils.weight_dtype(sd, diffusion_model_prefix) | |
| load_device = model_management.get_torch_device() | |
| model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix) | |
| if model_config is None: | |
| return None | |
| unet_weight_dtype = list(model_config.supported_inference_dtypes) | |
| if weight_dtype is not None and model_config.scaled_fp8 is None: | |
| unet_weight_dtype.append(weight_dtype) | |
| model_config.custom_operations = model_options.get("custom_operations", None) | |
| unet_dtype = model_options.get("dtype", model_options.get("weight_dtype", None)) | |
| if unet_dtype is None: | |
| unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype) | |
| manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes) | |
| model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) | |
| 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) | |
| model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device) | |
| model.load_model_weights(sd, diffusion_model_prefix) | |
| if output_vae: | |
| vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True) | |
| vae_sd = model_config.process_vae_state_dict(vae_sd) | |
| vae = VAE(sd=vae_sd) | |
| if output_clip: | |
| clip_target = model_config.clip_target(state_dict=sd) | |
| if clip_target is not None: | |
| clip_sd = model_config.process_clip_state_dict(sd) | |
| if len(clip_sd) > 0: | |
| parameters = comfy.utils.calculate_parameters(clip_sd) | |
| clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters, model_options=te_model_options) | |
| m, u = clip.load_sd(clip_sd, full_model=True) | |
| if len(m) > 0: | |
| m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m)) | |
| if len(m_filter) > 0: | |
| logging.warning("clip missing: {}".format(m)) | |
| else: | |
| logging.debug("clip missing: {}".format(m)) | |
| if len(u) > 0: | |
| logging.debug("clip unexpected {}:".format(u)) | |
| else: | |
| logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.") | |
| left_over = sd.keys() | |
| if len(left_over) > 0: | |
| logging.debug("left over keys: {}".format(left_over)) | |
| if output_model: | |
| model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device()) | |
| if inital_load_device != torch.device("cpu"): | |
| logging.info("loaded straight to GPU") | |
| model_management.load_models_gpu([model_patcher], force_full_load=True) | |
| return (model_patcher, clip, vae, clipvision) | |
| def load_diffusion_model_state_dict(sd, model_options={}): #load unet in diffusers or regular format | |
| dtype = model_options.get("dtype", None) | |
| #Allow loading unets from checkpoint files | |
| diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd) | |
| temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True) | |
| if len(temp_sd) > 0: | |
| sd = temp_sd | |
| parameters = comfy.utils.calculate_parameters(sd) | |
| weight_dtype = comfy.utils.weight_dtype(sd) | |
| load_device = model_management.get_torch_device() | |
| model_config = model_detection.model_config_from_unet(sd, "") | |
| if model_config is not None: | |
| new_sd = sd | |
| else: | |
| new_sd = model_detection.convert_diffusers_mmdit(sd, "") | |
| if new_sd is not None: #diffusers mmdit | |
| model_config = model_detection.model_config_from_unet(new_sd, "") | |
| if model_config is None: | |
| return None | |
| else: #diffusers unet | |
| model_config = model_detection.model_config_from_diffusers_unet(sd) | |
| if model_config is None: | |
| return None | |
| diffusers_keys = comfy.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: | |
| logging.warning("{} {}".format(diffusers_keys[k], k)) | |
| offload_device = model_management.unet_offload_device() | |
| unet_weight_dtype = list(model_config.supported_inference_dtypes) | |
| if weight_dtype is not None and model_config.scaled_fp8 is None: | |
| unet_weight_dtype.append(weight_dtype) | |
| if dtype is None: | |
| unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype) | |
| else: | |
| unet_dtype = dtype | |
| manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes) | |
| model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) | |
| model_config.custom_operations = model_options.get("custom_operations", model_config.custom_operations) | |
| if model_options.get("fp8_optimizations", False): | |
| model_config.optimizations["fp8"] = True | |
| 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: | |
| logging.info("left over keys in unet: {}".format(left_over)) | |
| return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device) | |
| def load_diffusion_model(unet_path, model_options={}): | |
| sd = comfy.utils.load_torch_file(unet_path) | |
| model = load_diffusion_model_state_dict(sd, model_options=model_options) | |
| if model is None: | |
| logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path)) | |
| raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path)) | |
| return model | |
| def load_unet(unet_path, dtype=None): | |
| print("WARNING: the load_unet function has been deprecated and will be removed please switch to: load_diffusion_model") | |
| return load_diffusion_model(unet_path, model_options={"dtype": dtype}) | |
| def load_unet_state_dict(sd, dtype=None): | |
| print("WARNING: the load_unet_state_dict function has been deprecated and will be removed please switch to: load_diffusion_model_state_dict") | |
| return load_diffusion_model_state_dict(sd, model_options={"dtype": dtype}) | |
| def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None, extra_keys={}): | |
| clip_sd = None | |
| load_models = [model] | |
| if clip is not None: | |
| load_models.append(clip.load_model()) | |
| clip_sd = clip.get_sd() | |
| vae_sd = None | |
| if vae is not None: | |
| vae_sd = vae.get_sd() | |
| model_management.load_models_gpu(load_models, force_patch_weights=True) | |
| 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_sd, clip_vision_sd) | |
| for k in extra_keys: | |
| sd[k] = extra_keys[k] | |
| for k in sd: | |
| t = sd[k] | |
| if not t.is_contiguous(): | |
| sd[k] = t.contiguous() | |
| comfy.utils.save_torch_file(sd, output_path, metadata=metadata) | |