644 lines
31 KiB
Python
644 lines
31 KiB
Python
import torch
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from enum import Enum
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import logging
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from comfy import model_management
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from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine
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from .ldm.cascade.stage_a import StageA
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from .ldm.cascade.stage_c_coder import StageC_coder
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from .ldm.audio.autoencoder import AudioOobleckVAE
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import yaml
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import comfy.utils
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from . import clip_vision
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from . import gligen
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from . import diffusers_convert
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from . import model_detection
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from . import sd1_clip
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from . import sdxl_clip
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import comfy.text_encoders.sd2_clip
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import comfy.text_encoders.sd3_clip
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import comfy.text_encoders.sa_t5
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import comfy.text_encoders.aura_t5
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import comfy.text_encoders.hydit
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import comfy.text_encoders.flux
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import comfy.model_patcher
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import comfy.lora
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import comfy.t2i_adapter.adapter
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import comfy.supported_models_base
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import comfy.taesd.taesd
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def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
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key_map = {}
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if model is not None:
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key_map = comfy.lora.model_lora_keys_unet(model.model, key_map)
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if clip is not None:
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key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map)
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loaded = comfy.lora.load_lora(lora, key_map)
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if model is not None:
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new_modelpatcher = model.clone()
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k = new_modelpatcher.add_patches(loaded, strength_model)
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else:
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k = ()
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new_modelpatcher = None
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if clip is not None:
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new_clip = clip.clone()
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k1 = new_clip.add_patches(loaded, strength_clip)
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else:
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k1 = ()
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new_clip = None
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k = set(k)
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k1 = set(k1)
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for x in loaded:
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if (x not in k) and (x not in k1):
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logging.warning("NOT LOADED {}".format(x))
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return (new_modelpatcher, new_clip)
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class CLIP:
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def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}):
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if no_init:
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return
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params = target.params.copy()
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clip = target.clip
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tokenizer = target.tokenizer
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load_device = model_management.text_encoder_device()
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offload_device = model_management.text_encoder_offload_device()
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params['device'] = offload_device
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dtype = model_management.text_encoder_dtype(load_device)
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params['dtype'] = dtype
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self.cond_stage_model = clip(**(params))
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for dt in self.cond_stage_model.dtypes:
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if not model_management.supports_cast(load_device, dt):
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load_device = offload_device
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self.tokenizer = tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
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self.patcher = comfy.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)
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self.layer_idx = None
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logging.debug("CLIP model load device: {}, offload device: {}".format(load_device, offload_device))
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def clone(self):
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n = CLIP(no_init=True)
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n.patcher = self.patcher.clone()
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n.cond_stage_model = self.cond_stage_model
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n.tokenizer = self.tokenizer
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n.layer_idx = self.layer_idx
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return n
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def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
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return self.patcher.add_patches(patches, strength_patch, strength_model)
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def clip_layer(self, layer_idx):
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self.layer_idx = layer_idx
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def tokenize(self, text, return_word_ids=False):
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return self.tokenizer.tokenize_with_weights(text, return_word_ids)
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def encode_from_tokens(self, tokens, return_pooled=False, return_dict=False):
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self.cond_stage_model.reset_clip_options()
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if self.layer_idx is not None:
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self.cond_stage_model.set_clip_options({"layer": self.layer_idx})
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if return_pooled == "unprojected":
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self.cond_stage_model.set_clip_options({"projected_pooled": False})
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self.load_model()
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o = self.cond_stage_model.encode_token_weights(tokens)
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cond, pooled = o[:2]
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if return_dict:
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out = {"cond": cond, "pooled_output": pooled}
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if len(o) > 2:
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for k in o[2]:
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out[k] = o[2][k]
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return out
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if return_pooled:
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return cond, pooled
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return cond
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def encode(self, text):
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tokens = self.tokenize(text)
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return self.encode_from_tokens(tokens)
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def load_sd(self, sd, full_model=False):
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if full_model:
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return self.cond_stage_model.load_state_dict(sd, strict=False)
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else:
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return self.cond_stage_model.load_sd(sd)
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def get_sd(self):
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sd_clip = self.cond_stage_model.state_dict()
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sd_tokenizer = self.tokenizer.state_dict()
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for k in sd_tokenizer:
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sd_clip[k] = sd_tokenizer[k]
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return sd_clip
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def load_model(self):
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model_management.load_model_gpu(self.patcher)
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return self.patcher
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def get_key_patches(self):
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return self.patcher.get_key_patches()
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class VAE:
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def __init__(self, sd=None, device=None, config=None, dtype=None):
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if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
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sd = diffusers_convert.convert_vae_state_dict(sd)
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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)
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self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype)
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self.downscale_ratio = 8
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self.upscale_ratio = 8
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self.latent_channels = 4
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self.output_channels = 3
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self.process_input = lambda image: image * 2.0 - 1.0
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self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
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self.working_dtypes = [torch.bfloat16, torch.float32]
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if config is None:
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if "decoder.mid.block_1.mix_factor" in sd:
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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}
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decoder_config = encoder_config.copy()
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decoder_config["video_kernel_size"] = [3, 1, 1]
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decoder_config["alpha"] = 0.0
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self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
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encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config},
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decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config})
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elif "taesd_decoder.1.weight" in sd:
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self.latent_channels = sd["taesd_decoder.1.weight"].shape[1]
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self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels)
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elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade
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self.first_stage_model = StageA()
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self.downscale_ratio = 4
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self.upscale_ratio = 4
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#TODO
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#self.memory_used_encode
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#self.memory_used_decode
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self.process_input = lambda image: image
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self.process_output = lambda image: image
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elif "backbone.1.0.block.0.1.num_batches_tracked" in sd: #effnet: encoder for stage c latent of stable cascade
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self.first_stage_model = StageC_coder()
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self.downscale_ratio = 32
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self.latent_channels = 16
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new_sd = {}
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for k in sd:
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new_sd["encoder.{}".format(k)] = sd[k]
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sd = new_sd
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elif "blocks.11.num_batches_tracked" in sd: #previewer: decoder for stage c latent of stable cascade
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self.first_stage_model = StageC_coder()
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self.latent_channels = 16
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new_sd = {}
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for k in sd:
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new_sd["previewer.{}".format(k)] = sd[k]
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sd = new_sd
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elif "encoder.backbone.1.0.block.0.1.num_batches_tracked" in sd: #combined effnet and previewer for stable cascade
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self.first_stage_model = StageC_coder()
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self.downscale_ratio = 32
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self.latent_channels = 16
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elif "decoder.conv_in.weight" in sd:
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#default SD1.x/SD2.x VAE parameters
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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}
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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
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ddconfig['ch_mult'] = [1, 2, 4]
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self.downscale_ratio = 4
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self.upscale_ratio = 4
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self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1]
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if 'quant_conv.weight' in sd:
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self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=4)
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else:
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self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
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encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig},
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decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig})
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elif "decoder.layers.1.layers.0.beta" in sd:
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self.first_stage_model = AudioOobleckVAE()
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self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype)
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self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * 2048) * model_management.dtype_size(dtype)
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self.latent_channels = 64
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self.output_channels = 2
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self.upscale_ratio = 2048
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self.downscale_ratio = 2048
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self.process_output = lambda audio: audio
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self.process_input = lambda audio: audio
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self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
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else:
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logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
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self.first_stage_model = None
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return
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else:
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self.first_stage_model = AutoencoderKL(**(config['params']))
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self.first_stage_model = self.first_stage_model.eval()
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m, u = self.first_stage_model.load_state_dict(sd, strict=False)
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if len(m) > 0:
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logging.warning("Missing VAE keys {}".format(m))
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if len(u) > 0:
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logging.debug("Leftover VAE keys {}".format(u))
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if device is None:
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device = model_management.vae_device()
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self.device = device
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offload_device = model_management.vae_offload_device()
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if dtype is None:
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dtype = model_management.vae_dtype(self.device, self.working_dtypes)
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self.vae_dtype = dtype
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self.first_stage_model.to(self.vae_dtype)
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self.output_device = model_management.intermediate_device()
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self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device)
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logging.debug("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype))
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def vae_encode_crop_pixels(self, pixels):
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dims = pixels.shape[1:-1]
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for d in range(len(dims)):
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x = (dims[d] // self.downscale_ratio) * self.downscale_ratio
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x_offset = (dims[d] % self.downscale_ratio) // 2
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if x != dims[d]:
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pixels = pixels.narrow(d + 1, x_offset, x)
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return pixels
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def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
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steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)
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steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap)
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steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
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pbar = comfy.utils.ProgressBar(steps)
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decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
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output = self.process_output(
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(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) +
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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) +
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comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar))
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/ 3.0)
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return output
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def decode_tiled_1d(self, samples, tile_x=128, overlap=32):
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decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
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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)
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def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
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steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
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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)
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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)
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pbar = comfy.utils.ProgressBar(steps)
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encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
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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)
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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)
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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)
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samples /= 3.0
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return samples
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def encode_tiled_1d(self, samples, tile_x=128 * 2048, overlap=32 * 2048):
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encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
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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)
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def decode(self, samples_in):
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try:
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memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
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model_management.load_models_gpu([self.patcher], memory_required=memory_used)
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free_memory = model_management.get_free_memory(self.device)
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batch_number = int(free_memory / memory_used)
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batch_number = max(1, batch_number)
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pixel_samples = torch.empty((samples_in.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples_in.shape[2:])), device=self.output_device)
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for x in range(0, samples_in.shape[0], batch_number):
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samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
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pixel_samples[x:x+batch_number] = self.process_output(self.first_stage_model.decode(samples).to(self.output_device).float())
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except model_management.OOM_EXCEPTION as e:
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logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
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if len(samples_in.shape) == 3:
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pixel_samples = self.decode_tiled_1d(samples_in)
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else:
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pixel_samples = self.decode_tiled_(samples_in)
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pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1)
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return pixel_samples
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def decode_tiled(self, samples, tile_x=64, tile_y=64, overlap = 16):
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model_management.load_model_gpu(self.patcher)
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output = self.decode_tiled_(samples, tile_x, tile_y, overlap)
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return output.movedim(1,-1)
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def encode(self, pixel_samples):
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pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
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pixel_samples = pixel_samples.movedim(-1,1)
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try:
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memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)
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model_management.load_models_gpu([self.patcher], memory_required=memory_used)
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free_memory = model_management.get_free_memory(self.device)
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batch_number = int(free_memory / memory_used)
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batch_number = max(1, batch_number)
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samples = torch.empty((pixel_samples.shape[0], self.latent_channels) + tuple(map(lambda a: a // self.downscale_ratio, pixel_samples.shape[2:])), device=self.output_device)
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for x in range(0, pixel_samples.shape[0], batch_number):
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pixels_in = self.process_input(pixel_samples[x:x+batch_number]).to(self.vae_dtype).to(self.device)
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samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
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except model_management.OOM_EXCEPTION as e:
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logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
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if len(pixel_samples.shape) == 3:
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samples = self.encode_tiled_1d(pixel_samples)
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else:
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samples = self.encode_tiled_(pixel_samples)
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return samples
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def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
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pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
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model_management.load_model_gpu(self.patcher)
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pixel_samples = pixel_samples.movedim(-1,1)
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samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap)
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return samples
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def get_sd(self):
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return self.first_stage_model.state_dict()
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class StyleModel:
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def __init__(self, model, device="cpu"):
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self.model = model
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def get_cond(self, input):
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return self.model(input.last_hidden_state)
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def load_style_model(ckpt_path):
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model_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
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keys = model_data.keys()
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if "style_embedding" in keys:
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model = comfy.t2i_adapter.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8)
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else:
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raise Exception("invalid style model {}".format(ckpt_path))
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model.load_state_dict(model_data)
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return StyleModel(model)
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class CLIPType(Enum):
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STABLE_DIFFUSION = 1
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STABLE_CASCADE = 2
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SD3 = 3
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STABLE_AUDIO = 4
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HUNYUAN_DIT = 5
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FLUX = 6
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def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION):
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clip_data = []
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for p in ckpt_paths:
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clip_data.append(comfy.utils.load_torch_file(p, safe_load=True))
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class EmptyClass:
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pass
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for i in range(len(clip_data)):
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if "transformer.resblocks.0.ln_1.weight" in clip_data[i]:
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clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "")
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else:
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if "text_projection" in clip_data[i]:
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clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node
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clip_target = EmptyClass()
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clip_target.params = {}
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if len(clip_data) == 1:
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if "text_model.encoder.layers.30.mlp.fc1.weight" in clip_data[0]:
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if clip_type == CLIPType.STABLE_CASCADE:
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clip_target.clip = sdxl_clip.StableCascadeClipModel
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clip_target.tokenizer = sdxl_clip.StableCascadeTokenizer
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else:
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clip_target.clip = sdxl_clip.SDXLRefinerClipModel
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clip_target.tokenizer = sdxl_clip.SDXLTokenizer
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elif "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data[0]:
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clip_target.clip = comfy.text_encoders.sd2_clip.SD2ClipModel
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clip_target.tokenizer = comfy.text_encoders.sd2_clip.SD2Tokenizer
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elif "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in clip_data[0]:
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weight = clip_data[0]["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"]
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dtype_t5 = weight.dtype
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if weight.shape[-1] == 4096:
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clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5)
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clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
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elif weight.shape[-1] == 2048:
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clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model
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clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer
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elif "encoder.block.0.layer.0.SelfAttention.k.weight" in clip_data[0]:
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clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model
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clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer
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else:
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clip_target.clip = sd1_clip.SD1ClipModel
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clip_target.tokenizer = sd1_clip.SD1Tokenizer
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elif len(clip_data) == 2:
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if clip_type == CLIPType.SD3:
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clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=True, t5=False)
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clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
|
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elif clip_type == CLIPType.HUNYUAN_DIT:
|
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clip_target.clip = comfy.text_encoders.hydit.HyditModel
|
|
clip_target.tokenizer = comfy.text_encoders.hydit.HyditTokenizer
|
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elif clip_type == CLIPType.FLUX:
|
|
weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight"
|
|
weight = clip_data[0].get(weight_name, clip_data[1].get(weight_name, None))
|
|
dtype_t5 = None
|
|
if weight is not None:
|
|
dtype_t5 = weight.dtype
|
|
|
|
clip_target.clip = comfy.text_encoders.flux.flux_clip(dtype_t5=dtype_t5)
|
|
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.SD3ClipModel
|
|
clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
|
|
|
|
clip = CLIP(clip_target, embedding_directory=embedding_directory)
|
|
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())
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|
|
|
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)
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|
|
|
def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True):
|
|
sd = comfy.utils.load_torch_file(ckpt_path)
|
|
sd_keys = sd.keys()
|
|
clip = None
|
|
clipvision = None
|
|
vae = None
|
|
model = None
|
|
model_patcher = None
|
|
clip_target = None
|
|
|
|
diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
|
|
parameters = comfy.utils.calculate_parameters(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:
|
|
raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path))
|
|
|
|
unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
|
|
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)
|
|
offload_device = model_management.unet_offload_device()
|
|
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:
|
|
clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd)
|
|
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(), current_device=inital_load_device)
|
|
if inital_load_device != torch.device("cpu"):
|
|
logging.info("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 or regular format
|
|
|
|
#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)
|
|
unet_dtype = model_management.unet_dtype(model_params=parameters)
|
|
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_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
|
|
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 = 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_unet(unet_path):
|
|
sd = comfy.utils.load_torch_file(unet_path)
|
|
model = load_unet_state_dict(sd)
|
|
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 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()
|
|
|
|
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.get_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)
|