237 lines
8.2 KiB
Python
237 lines
8.2 KiB
Python
import gc
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import html
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import os
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import re
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import torch
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import numpy as np
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from pathlib import Path
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import folder_paths
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NODE_DIR = Path(__file__).parent
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CONFIGS_DIR = NODE_DIR / "walkyrie_configs"
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_loaded_cache = {}
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def _split_state_dict(merged_sd):
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components = {"transformer": {}, "text_encoder": {}, "vae": {}}
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for k, v in merged_sd.items():
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for comp in components:
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if k.startswith(comp + "."):
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components[comp][k[len(comp) + 1:]] = v
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break
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return components
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def _prompt_clean(text):
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try:
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import ftfy
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text = ftfy.fix_text(text)
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except ImportError:
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pass
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text = html.unescape(html.unescape(text))
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text = re.sub(r"\s+", " ", text).strip()
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return text
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def _encode_prompt(tokenizer, text_encoder, prompts, max_length, device, dtype):
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prompts = [_prompt_clean(p) for p in prompts]
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tokens = tokenizer(
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prompts,
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padding="max_length",
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max_length=max_length,
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truncation=True,
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add_special_tokens=True,
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return_attention_mask=True,
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return_tensors="pt",
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)
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input_ids = tokens.input_ids.to(device)
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attention_mask = tokens.attention_mask.to(device)
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seq_lens = attention_mask.gt(0).sum(dim=1).long()
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with torch.no_grad():
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embeds = text_encoder(input_ids, attention_mask).last_hidden_state.to(dtype)
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embeds = [u[:v] for u, v in zip(embeds, seq_lens)]
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embeds = torch.stack(
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[torch.cat([u, u.new_zeros(max_length - u.size(0), u.size(1))]) for u in embeds], dim=0
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)
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return embeds
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def _unload_cached_models():
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global _loaded_cache
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if not _loaded_cache:
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return
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print("[Walkyrie] Clearing previous model from memory...")
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for key, models in _loaded_cache.items():
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for name in ("text_encoder", "vae", "transformer"):
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m = models.get(name)
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if m is not None:
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m.to("cpu")
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del m
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_loaded_cache.clear()
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gc.collect()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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print("[Walkyrie] Memory cleared (RAM + VRAM).")
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def _load_models(configs_dir, merged_file, torch_dtype):
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from safetensors.torch import load_file
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from transformers import AutoTokenizer, UMT5EncoderModel, AutoConfig
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from diffusers import AutoencoderKLWan, WanTransformer3DModel, FlowMatchEulerDiscreteScheduler
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cache_key = (merged_file, torch_dtype)
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if cache_key in _loaded_cache:
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print("[Walkyrie] Using cached models.")
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return _loaded_cache[cache_key]
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if _loaded_cache:
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_unload_cached_models()
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print(f"[Walkyrie] Loading: {merged_file}")
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merged_sd = load_file(merged_file, device="cpu")
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sd = _split_state_dict(merged_sd)
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tokenizer = AutoTokenizer.from_pretrained(configs_dir, subfolder="tokenizer")
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te_config = AutoConfig.from_pretrained(os.path.join(configs_dir, "text_encoder"))
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text_encoder = UMT5EncoderModel(te_config).to(torch_dtype)
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text_encoder.load_state_dict(sd["text_encoder"], strict=False)
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text_encoder.eval()
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vae_config = AutoencoderKLWan.load_config(configs_dir, subfolder="vae")
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vae = AutoencoderKLWan.from_config(vae_config).to(torch_dtype)
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vae.load_state_dict(sd["vae"], strict=False)
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vae.eval()
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tr_config = WanTransformer3DModel.load_config(configs_dir, subfolder="transformer")
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transformer = WanTransformer3DModel.from_config(tr_config).to(torch_dtype)
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transformer.load_state_dict(sd["transformer"], strict=False)
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transformer.eval()
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scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(configs_dir, subfolder="scheduler")
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models = dict(
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tokenizer = tokenizer,
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text_encoder = text_encoder,
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vae = vae,
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transformer = transformer,
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scheduler = scheduler,
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)
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_loaded_cache[cache_key] = models
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print("[Walkyrie] Models ready.")
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return models
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@torch.no_grad()
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def _run_inference(models, prompt, height, width, steps, guidance_scale, generator, device, dtype):
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try:
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from tqdm import tqdm
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_has_tqdm = True
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except ImportError:
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_has_tqdm = False
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from comfy.utils import ProgressBar
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tokenizer = models["tokenizer"]
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text_encoder = models["text_encoder"].to(device)
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vae = models["vae"].to(device)
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transformer = models["transformer"].to(device)
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scheduler = models["scheduler"]
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prompts = [prompt] if isinstance(prompt, str) else prompt
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batch_size = len(prompts)
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prompt_embeds = _encode_prompt(tokenizer, text_encoder, prompts, 226, device, dtype)
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negative_embeds = _encode_prompt(tokenizer, text_encoder, [""] * batch_size, 226, device, dtype)
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vae_scale = getattr(vae.config, "scale_factor_spatial", 8)
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in_channels = transformer.config.in_channels
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shape = (batch_size, in_channels, 1, height // vae_scale, width // vae_scale)
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latents = torch.randn(shape, generator=generator, device=device, dtype=dtype)
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scheduler.set_timesteps(steps, device=device)
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timesteps = scheduler.timesteps
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comfy_pbar = ProgressBar(steps)
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cli_iter = (
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tqdm(timesteps, desc="[Walkyrie] Sampling", unit="step", dynamic_ncols=True)
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if _has_tqdm
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else timesteps
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)
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for i, t in enumerate(cli_iter):
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latents_input = torch.cat([latents, latents])
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combined_embeds = torch.cat([negative_embeds, prompt_embeds])
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noise_pred = transformer(
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hidden_states=latents_input.to(dtype),
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timestep=t.expand(batch_size * 2),
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encoder_hidden_states=combined_embeds,
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return_dict=False,
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)[0]
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noise_uncond, noise_cond = noise_pred.chunk(2)
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noise_pred = noise_uncond + guidance_scale * (noise_cond - noise_uncond)
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latents = scheduler.step(noise_pred, t, latents, return_dict=False)[0]
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comfy_pbar.update(1)
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if not _has_tqdm:
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print(f"[Walkyrie] Step {i + 1}/{steps}")
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latents = latents.to(vae.dtype)
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latents_mean = torch.tensor(vae.config.latents_mean).view(1, vae.config.z_dim, 1, 1, 1).to(latents)
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latents_std = 1.0 / torch.tensor(vae.config.latents_std).view(1, vae.config.z_dim, 1, 1, 1).to(latents)
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latents = latents / latents_std + latents_mean
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image = vae.decode(latents, return_dict=False)[0]
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image = image.squeeze(2)
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image = (image / 2 + 0.5).clamp(0, 1)
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image = image.cpu().permute(0, 2, 3, 1).float()
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return image
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class WalkyrieNode:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"ckpt_name" : (folder_paths.get_filename_list("checkpoints"),),
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"prompt" : ("STRING", {"multiline": True, "default": ""}),
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"height" : ("INT", {"default": 1024, "min": 64, "max": 4096, "step": 16}),
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"width" : ("INT", {"default": 1024, "min": 64, "max": 4096, "step": 16}),
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"steps" : ("INT", {"default": 20, "min": 1, "max": 200}),
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"guidance_scale": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 20.0, "step": 0.1}),
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"seed" : ("INT", {"default": 42, "min": 0, "max": 0xffffffffffffffff}),
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"dtype" : (["bfloat16", "float16", "float32"], {"default": "bfloat16"}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "generate"
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CATEGORY = "Walkyrie"
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def generate(self, ckpt_name, prompt, height, width, steps, guidance_scale, seed, dtype):
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dtype_map = {"bfloat16": torch.bfloat16, "float16": torch.float16, "float32": torch.float32}
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torch_dtype = dtype_map[dtype]
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device = "cuda" if torch.cuda.is_available() else "cpu"
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merged_file = folder_paths.get_full_path("checkpoints", ckpt_name)
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models = _load_models(str(CONFIGS_DIR), merged_file, torch_dtype)
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generator = torch.Generator(device=device).manual_seed(seed)
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image = _run_inference(models, prompt, height, width, steps, guidance_scale, generator, device, torch_dtype)
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return (image,)
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NODE_CLASS_MAPPINGS = {
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"WalkyrieNode": WalkyrieNode,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"WalkyrieNode": "Walkyrie",
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} |