372 lines
12 KiB
Python
372 lines
12 KiB
Python
import os
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from types import SimpleNamespace
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from typing import List
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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 safetensors import safe_open
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from transformers import CLIPTokenizer
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import comfy.model_management
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import comfy.sample
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import comfy.utils
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import folder_paths
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class DecoderBlock(nn.Module):
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def __init__(self, embed_dim, num_heads):
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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,
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num_heads=num_heads,
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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,
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num_heads=num_heads,
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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, memory, self_attn_mask=None):
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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,
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key=x,
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value=x,
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attn_mask=self_attn_mask,
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need_weights=False,
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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(
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query=x, key=memory, value=memory, need_weights=False
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)
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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.embed_dim = vision_embed_dim
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self.hidden_dim = config.hidden_dim
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self.vocab_size = vocab_size
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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(self.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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[
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DecoderBlock(config.hidden_dim, config.num_heads)
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for _ in range(config.num_blocks)
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]
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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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def forward(self, image_features, token_embeddings, output_projection):
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# project and add positional embeddings to image features
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memory = self.projection(image_features)
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memory = memory + self.memory_pos_embedding
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# get causal mask for self-attention
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seq_len = token_embeddings.size(1)
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mask = self.causal_mask[:seq_len, :seq_len]
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# pass through decoder layers
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x = token_embeddings
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for layer in self.layers:
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x = layer(x, memory, self_attn_mask=mask)
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# project to vocabulary
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logits = output_projection(x)
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return logits
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def generate(
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self,
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image_features,
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temperature,
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tokenizer,
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output_projection,
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token_embedding_,
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pos_embedding_,
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seed=None,
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):
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# project and add positional embeddings to image features
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memory = self.projection(image_features)
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memory = memory + self.memory_pos_embedding
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# start with BOS token
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batch_size = image_features.size(0)
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current_tokens = torch.full(
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(batch_size, 1),
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tokenizer.bos_token_id,
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dtype=torch.long,
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device=image_features.device,
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)
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generated_tokens = [current_tokens]
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generator = torch.Generator(device=image_features.device)
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if seed is not None:
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generator.manual_seed(seed)
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for _ in range(self.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(
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current_tokens.size(1), device=current_tokens.device
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)
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pos_embeddings = pos_embedding_(positions)
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x = token_embeddings + pos_embeddings
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# get causal mask for self-attention
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seq_len = x.size(1)
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mask = self.causal_mask[:seq_len, :seq_len]
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# pass through decoder layers
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for layer in self.layers:
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x = layer(x, memory, self_attn_mask=mask)
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# get next token
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logits = output_projection(x[:, -1:])
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logits = logits / temperature # temperature
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probs = F.softmax(logits, dim=-1)
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next_token = torch.multinomial(probs.squeeze(1), 1, generator=generator)
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generated_tokens.append(next_token)
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current_tokens = torch.cat([current_tokens, next_token], dim=1)
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# stop if all sequences have EOS token
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if (next_token == tokenizer.eos_token_id).all():
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break
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return torch.cat(generated_tokens, dim=1)
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class CLIPtionModel(nn.Module):
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def __init__(self, config, clip, clip_vision):
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super().__init__()
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# store CLIP models
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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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self.vision_model = clip_vision.model.vision_model
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self.clip_text_projection = (
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clip.cond_stage_model.clip_l.transformer.text_projection
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)
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self.clip_visual_projection = clip_vision.model.visual_projection
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self.clip_vision = clip_vision
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self.clip_text = clip
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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(
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clip_embed_weight.clone(), requires_grad=False
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)
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def forward(self, images, captions=None):
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vision_outputs = self.vision_model(images)
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image_features = vision_outputs.last_hidden_state
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token_embeddings = None
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if captions is not None:
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token_embeddings = self.text_model.embeddings.token_embedding(captions)
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positions = torch.arange(captions.size(1), device=captions.device)
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pos_embeddings = self.text_model.embeddings.position_embedding(positions)
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token_embeddings = token_embeddings + pos_embeddings
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return self.captioner(image_features, token_embeddings, self.output_projection)
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def get_tokenizer(self) -> CLIPTokenizer:
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return self.tokenizer
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def generate(self, images, seed=42, temperature=0.7, best_of=1) -> List[str]:
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device = comfy.model_management.get_torch_device()
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image_outputs = self.clip_vision.encode_image(images)
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image_embeds = image_outputs.image_embeds
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image_features = image_outputs.last_hidden_state
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captions = []
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for image_idx in range(image_features.size(0)):
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candidates = []
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for _ in range(best_of):
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with torch.amp.autocast("cuda"):
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features = (
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image_features[image_idx]
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.unsqueeze(0)
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.to(device, dtype=torch.float16)
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)
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tokens = self.captioner.generate(
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features,
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temperature,
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self.tokenizer,
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self.output_projection,
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self.text_model.embeddings.token_embedding,
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self.text_model.embeddings.position_embedding,
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seed=seed,
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)
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seed += 1
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text = self.tokenizer.decode(
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tokens.squeeze(),
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skip_special_tokens=True,
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clean_up_tokenization_spaces=True,
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)
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candidates.append(text)
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if best_of == 1:
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captions.append(candidates[0])
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continue
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# calculate CLIP similarity for each candidate
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scored = []
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embeds = (
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image_embeds[image_idx].unsqueeze(0).to(device, dtype=torch.float16)
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)
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embeds = embeds / embeds.norm(dim=-1, keepdim=True)
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for text in candidates:
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comfy_tokens = self.clip_text.tokenize(text)
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_, text_embeds = self.clip_text.encode_from_tokens(
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comfy_tokens, return_pooled=True, return_dict=False
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)
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text_embeds = text_embeds.to(device, dtype=torch.float16)
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text_embeds = text_embeds / text_embeds.norm(dim=-1, keepdim=True)
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clip_sim = torch.sum(embeds * text_embeds, dim=-1)[0]
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print(f"({clip_sim.item()}) {text}")
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scored.append((clip_sim, text))
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# return the candidate with the highest similarity
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scored.sort(key=lambda x: x[0], reverse=True)
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captions.append(scored[0][1])
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return captions
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class CLIPtionLoader:
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CATEGORY = "pharmapsychotic"
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FUNCTION = "load"
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RETURN_TYPES = ("CLIPTION_MODEL",)
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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": (
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"CLIP",
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{"tooltip": "The CLIP model used for encoding the text."},
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),
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"clip_vision": ("CLIP_VISION",),
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}
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}
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def load(self, clip, clip_vision):
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state_dict = {}
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base_path = os.path.dirname(os.path.abspath(__file__))
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model_path = os.path.join(base_path, "CLIPtion_20241219_fp16.safetensors")
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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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config = SimpleNamespace(
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**{"hidden_dim": 768, "num_heads": 8, "num_blocks": 6, "max_length": 77}
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)
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model = CLIPtionModel(config, clip, clip_vision)
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model.captioner.load_state_dict(state_dict)
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model.eval()
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model.to(comfy.model_management.get_torch_device())
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return (model,)
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class CLIPtion:
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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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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"model": ("CLIPTION_MODEL", {"tooltip": "The CLIPtion model."}),
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"image": ("IMAGE",),
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}
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}
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def caption(self, model, image):
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captions = model.generate(image)
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return (captions,)
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class CLIPtionGenerate:
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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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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"model": ("CLIPTION_MODEL", {"tooltip": "The CLIPtion model."}),
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"image": ("IMAGE",),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "The random seed used for creating the caption."}),
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},
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"optional": {
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"temperature": (
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"FLOAT",
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{"default": 0.7, "tooltip": "Temperature for sampling."},
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),
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"best_of": (
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"INT",
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{"default": 1, "tooltip": "Number of options to evaluate."},
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),
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},
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}
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def caption(self, model, image, seed, temperature=0.7, best_of=1):
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with torch.inference_mode():
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captions = model.generate(image, seed, temperature, best_of)
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return ([captions],)
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NODE_CLASS_MAPPINGS = {
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"CLIPtionLoader": CLIPtionLoader,
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"CLIPtionGenerate": CLIPtionGenerate,
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"CLIPtion": CLIPtion,
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}
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