easygoing0114
fa4b788000
new file: README.md new file: __init__.py new file: gitignore.txt new file: nodes.py new file: pyproject.toml
374 lines
15 KiB
Python
374 lines
15 KiB
Python
import logging
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import os
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import re
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from types import SimpleNamespace
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from typing import Optional, Tuple
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from huggingface_hub import hf_hub_download
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from safetensors import safe_open
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import comfy.model_management
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def _fix_punctuation_spacing(text: str) -> str:
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"""Remove stray whitespace before commas and periods left over from
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CLIPTokenizer's BPE decoding (e.g. "cats , dogs ." -> "cats, dogs.")."""
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return re.sub(r"\s+([,.])", r"\1", text)
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class DecoderBlock(nn.Module):
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def __init__(self, embed_dim: int, num_heads: int):
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super().__init__()
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self.norm1 = nn.LayerNorm(embed_dim)
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self.self_attn = nn.MultiheadAttention(
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embed_dim=embed_dim, num_heads=num_heads, batch_first=True
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)
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self.norm2 = nn.LayerNorm(embed_dim)
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self.cross_attn = nn.MultiheadAttention(
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embed_dim=embed_dim, num_heads=num_heads, batch_first=True
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)
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self.norm3 = nn.LayerNorm(embed_dim)
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self.mlp = nn.Sequential(
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nn.Linear(embed_dim, embed_dim * 4),
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nn.GELU(),
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nn.Identity(),
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nn.Linear(embed_dim * 4, embed_dim),
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)
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def forward(self, x: torch.Tensor, memory: torch.Tensor, self_attn_mask: torch.Tensor):
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# self attention with mask
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residual = x
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x = self.norm1(x)
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attn_output, _ = self.self_attn(
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query=x, key=x, value=x, attn_mask=self_attn_mask, need_weights=False, is_causal=True
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)
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x = residual + attn_output
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# cross attention
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residual = x
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x = self.norm2(x)
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attn_output, _ = self.cross_attn(query=x, key=memory, value=memory, need_weights=False)
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x = residual + attn_output
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# FFN
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residual = x
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x = self.norm3(x)
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x = residual + self.mlp(x)
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return x
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class Captioner(nn.Module):
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def __init__(self, config, vision_embed_dim: int, vocab_size: int):
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super().__init__()
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self.hidden_dim = config.hidden_dim
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self.max_length = config.max_length
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# projection from ViT dimension to decoder dimension
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self.projection = nn.Linear(vision_embed_dim, self.hidden_dim)
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self.memory_pos_embedding = nn.Parameter(torch.zeros(1, 257, self.hidden_dim))
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# decoder layers
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self.layers = nn.ModuleList(
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[DecoderBlock(config.hidden_dim, config.num_heads) for _ in range(config.num_blocks)]
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)
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causal_mask = nn.Transformer.generate_square_subsequent_mask(self.max_length)
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self.register_buffer("causal_mask", causal_mask, persistent=False)
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class CLIPtionModel(nn.Module):
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def __init__(self, config, clip, clip_vision, device=None):
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super().__init__()
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if not hasattr(clip, "cond_stage_model"):
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raise ValueError("CLIP is missing from model checkpoint")
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if not hasattr(clip.cond_stage_model, "clip_l"):
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raise ValueError("Must use model which includes CLIP-L")
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# store CLIP model references
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self.clip_text = clip
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self.clip_vision = clip_vision
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# use specified device, or fall back to ComfyUI default at runtime
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self.inference_device = device
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self.tokenizer = clip.tokenizer.clip_l.tokenizer
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self.text_model = clip.cond_stage_model.clip_l.transformer.text_model
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# clip.cond_stage_model.clip_l.transformer.text_projection is empty
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# so load a copy from the CLIPtion safetensors file instead
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self.text_projection = nn.Linear(768, 768, bias=False)
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# create caption decoder
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self.captioner = Captioner(config, 1024, self.tokenizer.vocab_size)
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# use CLIP's token embeddings for output projection
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clip_embed_weight = self.text_model.embeddings.token_embedding.weight
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self.output_projection = nn.Linear(
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self.captioner.hidden_dim, self.tokenizer.vocab_size, bias=False
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)
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self.output_projection.weight = nn.Parameter(clip_embed_weight.clone())
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def generate_beam(self, images: torch.Tensor, beam_width: int = 4) -> list:
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device = self.inference_device or comfy.model_management.get_torch_device()
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image_features, image_embeds = self._images_to_embeds(images, device)
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captions = []
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for image_idx in range(image_features.size(0)):
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features = image_features[image_idx].unsqueeze(0)
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candidates = self._beam_search(
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features, image_embeds[image_idx : image_idx + 1], device, beam_width
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)
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# pick highest scoring candidate
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candidates.sort(key=lambda x: x[0])
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for score, text in candidates:
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logging.debug(f"({score:.3f}) {text}")
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captions.append(candidates[-1][1])
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return captions
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def _beam_search(
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self,
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image_features: torch.Tensor,
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image_embed: torch.Tensor,
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device: torch.device,
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beam_width: int,
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):
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tokenizer = self.tokenizer
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captioner = self.captioner
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token_embedding = self.text_model.embeddings.token_embedding
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pos_embedding = self.text_model.embeddings.position_embedding
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vocab_size = tokenizer.vocab_size
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# project image features
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# determine dtype from captioner weights to handle FP32 models correctly
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model_dtype = next(captioner.parameters()).dtype
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memory = captioner.projection(image_features.to(dtype=model_dtype))
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memory = memory + captioner.memory_pos_embedding
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# start with beam_width copies of BOS token
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current_tokens = torch.full(
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(beam_width, 1), tokenizer.bos_token_id, dtype=torch.long, device=device
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)
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scores = torch.zeros(beam_width, device=device)
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for step in range(captioner.max_length - 2):
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# embed current tokens
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token_embeddings = token_embedding(current_tokens)
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positions = torch.arange(current_tokens.size(1), device=device)
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pos_embeddings = pos_embedding(positions)
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# cast to model dtype to handle FP32/FP16 mismatch
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x = (token_embeddings + pos_embeddings).to(dtype=model_dtype)
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# run decoder layers
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seq_len = x.size(1)
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mask = captioner.causal_mask[:seq_len, :seq_len]
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for layer in captioner.layers:
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x = layer(x, memory.repeat(beam_width, 1, 1), self_attn_mask=mask)
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# get next token log probabilities
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logits = self.output_projection(x[:, -1:])
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log_probs = F.log_softmax(logits, dim=-1)
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if step == 0:
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# pick top-k tokens for first step
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scores = log_probs.squeeze(1)[0]
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scores, indices = scores.topk(beam_width)
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current_tokens = torch.cat(
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[current_tokens[0:1].repeat(beam_width, 1), indices.unsqueeze(1)], dim=1
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)
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else:
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# calculate scores for next tokens [beam_width x vocab_size]
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next_scores = scores.unsqueeze(1) + log_probs.squeeze(1)
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# force sequences to continue EOS after first one
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prev_is_eos = current_tokens[:, -1] == tokenizer.eos_token_id
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vocab_mask = torch.zeros_like(next_scores)
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vocab_mask[prev_is_eos] = float("-inf")
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vocab_mask[prev_is_eos, tokenizer.eos_token_id] = 0
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next_scores = next_scores + vocab_mask
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# pick top beam_width sequences
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next_scores = next_scores.view(-1)
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scores, indices = next_scores.topk(beam_width)
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beam_indices = indices // vocab_size # which sequence each came from
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token_indices = indices % vocab_size # which token to append
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current_tokens = torch.cat(
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[current_tokens[beam_indices], token_indices.unsqueeze(1)], dim=1
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)
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# check if all beams ended with EOS
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if (current_tokens[:, -1] == tokenizer.eos_token_id).all():
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break
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# add final EOS token
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current_tokens = torch.cat(
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[current_tokens, torch.full((beam_width, 1), tokenizer.eos_token_id, device=device)],
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dim=1,
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)
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# rank final candidates by CLIP similarity
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candidates = []
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for idx in range(beam_width):
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tokens = current_tokens[idx]
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# trim everything after the first EOS token (inclusive) before decoding
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eos_positions = (tokens == tokenizer.eos_token_id).nonzero(as_tuple=True)[0]
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if len(eos_positions) > 0:
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tokens = tokens[: eos_positions[0]]
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text = tokenizer.decode(tokens, skip_special_tokens=True, clean_up_tokenization_spaces=True)
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text = _fix_punctuation_spacing(text)
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text_embeds = self._text_to_embed(text, device)
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clip_sim = torch.sum(image_embed * text_embeds, dim=-1)[0]
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candidates.append((clip_sim.item(), text))
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return candidates
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def _images_to_embeds(self, images: torch.Tensor, device: torch.device) -> Tuple[torch.Tensor, torch.Tensor]:
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if images.size(-1) == 1:
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images = images.repeat(1, 1, 1, 3)
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elif images.size(-1) == 4:
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images = images[..., :3]
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outputs = self.clip_vision.encode_image(images)
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# features go into the FP16 CLIPtion decoder, so cast to FP16
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features = outputs.last_hidden_state.to(device, dtype=torch.float16)
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if features.size(2) != 1024:
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raise ValueError(
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f"Expected image features to have 1024 dimensions but got {features.size(2)}. Please ensure you are using CLIP L."
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)
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# embeds are used only for CLIP similarity scoring, preserve original dtype (FP32) for accuracy
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embeds = outputs.image_embeds.to(device)
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embeds = embeds / embeds.norm(dim=-1, keepdim=True)
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return features, embeds
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def _text_to_embed(self, text: str, device: torch.device) -> torch.Tensor:
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# load CLIP model and disable final projection since that's missing from comfy checkpoints
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self.clip_text.load_model()
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self.clip_text.cond_stage_model.reset_clip_options()
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self.clip_text.cond_stage_model.set_clip_options({"projected_pooled": False})
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# calculate text embedding
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tokens = self.clip_text.tokenize(text)
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clip_l = self.clip_text.cond_stage_model.clip_l
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_, pooled = clip_l.encode_token_weights(tokens["l"])
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# preserve original dtype (FP32) for accurate CLIP similarity scoring
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text_embeds = self.text_projection(pooled.to(device))
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text_embeds = text_embeds / text_embeds.norm(dim=-1, keepdim=True)
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return text_embeds
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class CLIPtionBeamSearchIntegrated:
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"""
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Combined CLIPtion loader + beam search node.
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Loads the CLIPtion decoder on demand at caption time rather than at
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workflow-load time, and can optionally unload it from VRAM afterwards.
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The CLIPtion safetensors weights are kept on CPU between runs so that
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re-loading does not require a disk read.
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"""
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CATEGORY = "pharmapsychotic"
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FUNCTION = "caption"
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OUTPUT_IS_LIST = (True,)
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RETURN_TYPES = ("STRING",)
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# CLIPtion decoder config (fixed for the released checkpoint)
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_CAPTIONER_CONFIG = SimpleNamespace(hidden_dim=768, num_heads=8, num_blocks=6, max_length=77)
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_SAFETENSORS_FILE = "CLIPtion_20241219_fp16.safetensors"
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_HF_REPO_ID = "pharmapsychotic/CLIPtion"
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_HF_REVISION = "15ee8cb77a902616478a033332011ff640e72277"
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def __init__(self):
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self._model: Optional[CLIPtionModel] = None
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"clip": ("CLIP", {"tooltip": "CLIP text encoder (must include CLIP-L)."}),
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"clip_vision": ("CLIP_VISION", {"tooltip": "CLIP vision encoder (must be CLIP-L)."}),
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"image": ("IMAGE",),
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"beam_width": (
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"INT",
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{"default": 4, "min": 1, "max": 64, "tooltip": "Number of beams to maintain during search."},
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),
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"unload_after_run": (
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"BOOLEAN",
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{"default": False, "tooltip": "Unload CLIPtion decoder from VRAM after captioning."},
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),
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},
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"optional": {
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"device": (["default", "cpu"], {"advanced": True}),
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},
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}
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def caption(self, clip, clip_vision, image: torch.Tensor, beam_width: int = 4,
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unload_after_run: bool = False, device: str = "default"):
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try:
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self._load_model(clip, clip_vision, device)
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with torch.inference_mode():
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captions = self._model.generate_beam(image, beam_width)
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finally:
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if unload_after_run:
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self._unload_model()
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return (captions,)
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def _load_model(self, clip, clip_vision, device: str):
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"""Load CLIPtion decoder from disk and move to target device."""
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if self._model is not None:
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return
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base_path = os.path.dirname(os.path.abspath(__file__))
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local_path = os.path.join(base_path, self._SAFETENSORS_FILE)
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if os.path.exists(local_path):
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model_path = local_path
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else:
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model_path = hf_hub_download(
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repo_id=self._HF_REPO_ID, filename=self._SAFETENSORS_FILE, revision=self._HF_REVISION
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)
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state_dict = {}
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with safe_open(model_path, framework="pt", device="cpu") as f:
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for key in f.keys():
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state_dict[key] = f.get_tensor(key)
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tp_dict = {"weight": state_dict.pop("text_projection.weight")}
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inference_device = torch.device("cpu") if device == "cpu" else None
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model = CLIPtionModel(self._CAPTIONER_CONFIG, clip, clip_vision, device=inference_device)
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model.captioner.load_state_dict(state_dict)
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model.text_projection.load_state_dict(tp_dict)
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model.eval()
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load_device = inference_device or comfy.model_management.get_torch_device()
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model.to(load_device, dtype=torch.float16)
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# keep text_projection in FP32 to preserve CLIP similarity scoring accuracy
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model.text_projection.to(dtype=torch.float32)
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self._model = model
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logging.info(f"CLIPtionBeamSearchIntegrated: decoder loaded on {load_device}")
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def _unload_model(self):
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"""Remove the CLIPtion decoder from VRAM and CPU RAM completely."""
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if self._model is None:
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return
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del self._model
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self._model = None
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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logging.info("CLIPtionBeamSearchIntegrated: decoder unloaded")
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NODE_CLASS_MAPPINGS = {
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"CLIPtionBeamSearchIntegrated": CLIPtionBeamSearchIntegrated,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"CLIPtionBeamSearchIntegrated": "CLIPtion Beam Search (Integrated)",
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}
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