492 lines
21 KiB
Python
492 lines
21 KiB
Python
import torch
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import contextlib
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import math
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from comfy import model_management
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from .ldm.util import instantiate_from_config
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from .ldm.models.autoencoder import AutoencoderKL
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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_base
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from . import model_detection
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from . import sd1_clip
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from . import sd2_clip
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from . import sdxl_clip
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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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def load_model_weights(model, sd):
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m, u = model.load_state_dict(sd, strict=False)
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m = set(m)
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unexpected_keys = set(u)
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k = list(sd.keys())
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for x in k:
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if x not in unexpected_keys:
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w = sd.pop(x)
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del w
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if len(m) > 0:
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print("missing", m)
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return model
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def load_clip_weights(model, sd):
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k = list(sd.keys())
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for x in k:
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if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."):
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y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.")
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sd[y] = sd.pop(x)
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if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in sd:
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ids = sd['cond_stage_model.transformer.text_model.embeddings.position_ids']
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if ids.dtype == torch.float32:
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sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()
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sd = comfy.utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24)
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return load_model_weights(model, sd)
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def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
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key_map = comfy.lora.model_lora_keys_unet(model.model)
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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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new_modelpatcher = model.clone()
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k = new_modelpatcher.add_patches(loaded, strength_model)
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new_clip = clip.clone()
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k1 = new_clip.add_patches(loaded, strength_clip)
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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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print("NOT LOADED", 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):
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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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if model_management.should_use_fp16(load_device, prioritize_performance=False):
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params['dtype'] = torch.float16
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else:
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params['dtype'] = torch.float32
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self.cond_stage_model = clip(**(params))
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self.tokenizer = tokenizer(embedding_directory=embedding_directory)
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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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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):
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if self.layer_idx is not None:
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self.cond_stage_model.clip_layer(self.layer_idx)
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else:
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self.cond_stage_model.reset_clip_layer()
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self.load_model()
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cond, pooled = self.cond_stage_model.encode_token_weights(tokens)
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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):
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return self.cond_stage_model.load_sd(sd)
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def get_sd(self):
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return self.cond_stage_model.state_dict()
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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, ckpt_path=None, device=None, config=None):
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if config is None:
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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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self.first_stage_model = AutoencoderKL(ddconfig, {'target': 'torch.nn.Identity'}, 4, monitor="val/rec_loss")
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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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if ckpt_path is not None:
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sd = comfy.utils.load_torch_file(ckpt_path)
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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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m, u = self.first_stage_model.load_state_dict(sd, strict=False)
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if len(m) > 0:
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print("Missing VAE keys", m)
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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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self.offload_device = model_management.vae_offload_device()
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self.vae_dtype = model_management.vae_dtype()
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self.first_stage_model.to(self.vae_dtype)
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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)) + 1.0).float()
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output = torch.clamp((
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(comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8, pbar = pbar) +
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comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8, pbar = pbar) +
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comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = 8, pbar = pbar))
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/ 3.0) / 2.0, min=0.0, max=1.0)
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return output
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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((2. * a - 1.).to(self.vae_dtype).to(self.device)).sample().float()
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samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
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samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
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samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
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samples /= 3.0
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return samples
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def decode(self, samples_in):
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self.first_stage_model = self.first_stage_model.to(self.device)
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try:
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memory_used = (2562 * samples_in.shape[2] * samples_in.shape[3] * 64) * 1.7
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model_management.free_memory(memory_used, self.device)
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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], 3, round(samples_in.shape[2] * 8), round(samples_in.shape[3] * 8)), device="cpu")
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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] = torch.clamp((self.first_stage_model.decode(samples).cpu().float() + 1.0) / 2.0, min=0.0, max=1.0)
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except model_management.OOM_EXCEPTION as e:
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print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
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pixel_samples = self.decode_tiled_(samples_in)
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self.first_stage_model = self.first_stage_model.to(self.offload_device)
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pixel_samples = pixel_samples.cpu().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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self.first_stage_model = self.first_stage_model.to(self.device)
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output = self.decode_tiled_(samples, tile_x, tile_y, overlap)
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self.first_stage_model = self.first_stage_model.to(self.offload_device)
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return output.movedim(1,-1)
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def encode(self, pixel_samples):
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self.first_stage_model = self.first_stage_model.to(self.device)
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pixel_samples = pixel_samples.movedim(-1,1)
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try:
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memory_used = (2078 * pixel_samples.shape[2] * pixel_samples.shape[3]) * 1.7 #NOTE: this constant along with the one in the decode above are estimated from the mem usage for the VAE and could change.
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model_management.free_memory(memory_used, self.device)
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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], 4, round(pixel_samples.shape[2] // 8), round(pixel_samples.shape[3] // 8)), device="cpu")
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for x in range(0, pixel_samples.shape[0], batch_number):
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pixels_in = (2. * pixel_samples[x:x+batch_number] - 1.).to(self.vae_dtype).to(self.device)
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samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).sample().cpu().float()
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except model_management.OOM_EXCEPTION as e:
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print("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
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samples = self.encode_tiled_(pixel_samples)
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self.first_stage_model = self.first_stage_model.to(self.offload_device)
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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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self.first_stage_model = self.first_stage_model.to(self.device)
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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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self.first_stage_model = self.first_stage_model.to(self.offload_device)
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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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def load_clip(ckpt_paths, embedding_directory=None):
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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.transformers_convert(clip_data[i], "", "text_model.", 32)
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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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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 = sd2_clip.SD2ClipModel
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clip_target.tokenizer = sd2_clip.SD2Tokenizer
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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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else:
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clip_target.clip = sdxl_clip.SDXLClipModel
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clip_target.tokenizer = sdxl_clip.SDXLTokenizer
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clip = CLIP(clip_target, embedding_directory=embedding_directory)
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for c in clip_data:
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m, u = clip.load_sd(c)
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if len(m) > 0:
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print("clip missing:", m)
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if len(u) > 0:
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print("clip unexpected:", u)
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return clip
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def load_gligen(ckpt_path):
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data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
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model = gligen.load_gligen(data)
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if model_management.should_use_fp16():
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model = model.half()
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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):
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#TODO: this function is a mess and should be removed eventually
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if config is None:
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with open(config_path, 'r') as stream:
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config = yaml.safe_load(stream)
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model_config_params = config['model']['params']
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clip_config = model_config_params['cond_stage_config']
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scale_factor = model_config_params['scale_factor']
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vae_config = model_config_params['first_stage_config']
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fp16 = False
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if "unet_config" in model_config_params:
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if "params" in model_config_params["unet_config"]:
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unet_config = model_config_params["unet_config"]["params"]
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if "use_fp16" in unet_config:
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fp16 = unet_config["use_fp16"]
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noise_aug_config = None
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if "noise_aug_config" in model_config_params:
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noise_aug_config = model_config_params["noise_aug_config"]
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model_type = model_base.ModelType.EPS
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if "parameterization" in model_config_params:
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if model_config_params["parameterization"] == "v":
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model_type = model_base.ModelType.V_PREDICTION
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clip = None
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vae = None
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class WeightsLoader(torch.nn.Module):
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pass
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if state_dict is None:
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state_dict = comfy.utils.load_torch_file(ckpt_path)
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class EmptyClass:
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pass
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model_config = comfy.supported_models_base.BASE({})
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from . import latent_formats
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model_config.latent_format = latent_formats.SD15(scale_factor=scale_factor)
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model_config.unet_config = unet_config
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if config['model']["target"].endswith("ImageEmbeddingConditionedLatentDiffusion"):
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model = model_base.SD21UNCLIP(model_config, noise_aug_config["params"], model_type=model_type)
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else:
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model = model_base.BaseModel(model_config, model_type=model_type)
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if config['model']["target"].endswith("LatentInpaintDiffusion"):
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model.set_inpaint()
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if fp16:
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model = model.half()
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offload_device = model_management.unet_offload_device()
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model = model.to(offload_device)
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model.load_model_weights(state_dict, "model.diffusion_model.")
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if output_vae:
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w = WeightsLoader()
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vae = VAE(config=vae_config)
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w.first_stage_model = vae.first_stage_model
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load_model_weights(w, state_dict)
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if output_clip:
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w = WeightsLoader()
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clip_target = EmptyClass()
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clip_target.params = clip_config.get("params", {})
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if clip_config["target"].endswith("FrozenOpenCLIPEmbedder"):
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clip_target.clip = sd2_clip.SD2ClipModel
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clip_target.tokenizer = sd2_clip.SD2Tokenizer
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elif clip_config["target"].endswith("FrozenCLIPEmbedder"):
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clip_target.clip = sd1_clip.SD1ClipModel
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clip_target.tokenizer = sd1_clip.SD1Tokenizer
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clip = CLIP(clip_target, embedding_directory=embedding_directory)
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w.cond_stage_model = clip.cond_stage_model
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load_clip_weights(w, state_dict)
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return (comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), 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):
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sd = comfy.utils.load_torch_file(ckpt_path)
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sd_keys = sd.keys()
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clip = None
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clipvision = None
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vae = None
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model = None
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model_patcher = None
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clip_target = None
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parameters = comfy.utils.calculate_parameters(sd, "model.diffusion_model.")
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fp16 = model_management.should_use_fp16(model_params=parameters)
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class WeightsLoader(torch.nn.Module):
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pass
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model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.", fp16)
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if model_config is None:
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raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path))
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if model_config.clip_vision_prefix is not None:
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if output_clipvision:
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clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True)
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dtype = torch.float32
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if fp16:
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dtype = torch.float16
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if output_model:
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inital_load_device = model_management.unet_inital_load_device(parameters, dtype)
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offload_device = model_management.unet_offload_device()
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model = model_config.get_model(sd, "model.diffusion_model.", device=inital_load_device)
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model.load_model_weights(sd, "model.diffusion_model.")
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if output_vae:
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vae = VAE()
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w = WeightsLoader()
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w.first_stage_model = vae.first_stage_model
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load_model_weights(w, sd)
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if output_clip:
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w = WeightsLoader()
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clip_target = model_config.clip_target()
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clip = CLIP(clip_target, embedding_directory=embedding_directory)
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w.cond_stage_model = clip.cond_stage_model
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sd = model_config.process_clip_state_dict(sd)
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load_model_weights(w, sd)
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|
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left_over = sd.keys()
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if len(left_over) > 0:
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print("left over keys:", left_over)
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|
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if output_model:
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model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device(), current_device=inital_load_device)
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if inital_load_device != torch.device("cpu"):
|
|
print("loaded straight to GPU")
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|
model_management.load_model_gpu(model_patcher)
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|
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return (model_patcher, clip, vae, clipvision)
|
|
|
|
|
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def load_unet(unet_path): #load unet in diffusers format
|
|
sd = comfy.utils.load_torch_file(unet_path)
|
|
parameters = comfy.utils.calculate_parameters(sd)
|
|
fp16 = model_management.should_use_fp16(model_params=parameters)
|
|
if "input_blocks.0.0.weight" in sd: #ldm
|
|
model_config = model_detection.model_config_from_unet(sd, "", fp16)
|
|
if model_config is None:
|
|
raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
|
|
new_sd = sd
|
|
|
|
else: #diffusers
|
|
model_config = model_detection.model_config_from_diffusers_unet(sd, fp16)
|
|
if model_config is None:
|
|
print("ERROR UNSUPPORTED UNET", unet_path)
|
|
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:
|
|
print(diffusers_keys[k], k)
|
|
offload_device = model_management.unet_offload_device()
|
|
model = model_config.get_model(new_sd, "")
|
|
model = model.to(offload_device)
|
|
model.load_model_weights(new_sd, "")
|
|
return comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device)
|
|
|
|
def save_checkpoint(output_path, model, clip, vae, metadata=None):
|
|
model_management.load_models_gpu([model, clip.load_model()])
|
|
sd = model.model.state_dict_for_saving(clip.get_sd(), vae.get_sd())
|
|
comfy.utils.save_torch_file(sd, output_path, metadata=metadata)
|