mirror of
https://github.com/comfyanonymous/ComfyUI.git
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8e53f001a4
* Initial HiDream01-image support * Cleanup nodes * Cleaner handling of empty placeholder models * Remove snap_to_predefined, prefer tooltip for the trained resolutions * Add model and block wrappers * Fix shift tooltip * Add node to work around the patch tile issue Experimental, runs multiple passes with the patch grid offset and blends with various different methods. * Qwen35 vision rotary_pos_emb cast fix * Fix embedding layout type * Some small optimizations * Cleanup, don't need this fallback * Prefix KV cache, cleanup Bit of speed, reduce redundant code * Get rid of redundant custom sampler, refactor noise scaling Our existing lcm sampler is mathematically same, just added the missing options to it instead and a node to control them. Refactored the noise scaling and fix it for the stochastic samplers, add a generic node to control the initial noise scale. * Update nodes_hidream_o1.py * Fix some cache validation cases * Keep existing sampling params * Remove redundant video vision path * Replace some numpy ops with torch * Fx RoPE index for batch size > 1 * Prefer torch preprocessing * Rename block_type to be compatible with existing patch nodes * Fixes and tweaks
120 lines
3.9 KiB
Python
120 lines
3.9 KiB
Python
"""HiDream-O1-Image tokenizer-only text encoder.
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The real Qwen3-VL backbone runs inside diffusion_model.* every step, so this
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module just tokenizes the prompt into text_input_ids and emits them as
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conditioning. Position ids / token_types / vinput_mask depend on target H/W
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and are built later in model_base.HiDreamO1.extra_conds.
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"""
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import os
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import torch
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from transformers import Qwen2Tokenizer
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from comfy import sd1_clip
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# Qwen3-VL special tokens
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IM_START_ID = 151644
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IM_END_ID = 151645
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ASSISTANT_ID = 77091
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USER_ID = 872
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NEWLINE_ID = 198
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VISION_START_ID = 151652
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VISION_END_ID = 151653
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IMAGE_TOKEN_ID = 151655
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VIDEO_TOKEN_ID = 151656
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# HiDream-O1-specific tokens
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BOI_TOKEN_ID = 151669
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BOR_TOKEN_ID = 151670
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EOR_TOKEN_ID = 151671
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BOT_TOKEN_ID = 151672
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TMS_TOKEN_ID = 151673
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class HiDreamO1QwenTokenizer(sd1_clip.SDTokenizer):
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def __init__(self, embedding_directory=None, tokenizer_data={}):
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tokenizer_path = os.path.join(
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os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer"
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)
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super().__init__(
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tokenizer_path,
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pad_with_end=False,
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embedding_size=4096,
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embedding_key="hidream_o1",
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tokenizer_class=Qwen2Tokenizer,
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has_start_token=False,
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has_end_token=False,
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pad_to_max_length=False,
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max_length=99999999,
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min_length=1,
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pad_token=151643,
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tokenizer_data=tokenizer_data,
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)
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class HiDreamO1Tokenizer(sd1_clip.SD1Tokenizer):
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"""Wraps prompt in the upstream chat template ending with boi/tms markers.
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Image tokens get spliced in at sample time once target H/W is known.
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"""
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def __init__(self, embedding_directory=None, tokenizer_data={}):
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super().__init__(
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embedding_directory=embedding_directory,
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tokenizer_data=tokenizer_data,
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name="hidream_o1",
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tokenizer=HiDreamO1QwenTokenizer,
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)
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def tokenize_with_weights(self, text, return_word_ids=False, **kwargs):
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text_tokens_dict = super().tokenize_with_weights(
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text, return_word_ids=return_word_ids, disable_weights=True, **kwargs
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)
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text_tuples = text_tokens_dict["hidream_o1"][0]
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text_tuples = [t for t in text_tuples if int(t[0]) != 151643] # strip pad
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# <|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant\n<|boi|><|tms|>
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def tok(tid):
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return (tid, 1.0) if not return_word_ids else (tid, 1.0, 0)
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prefix = [tok(IM_START_ID), tok(USER_ID), tok(NEWLINE_ID)]
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suffix = [
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tok(IM_END_ID), tok(NEWLINE_ID),
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tok(IM_START_ID), tok(ASSISTANT_ID), tok(NEWLINE_ID),
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tok(BOI_TOKEN_ID), tok(TMS_TOKEN_ID),
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]
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full = prefix + list(text_tuples) + suffix
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return {"hidream_o1": [full]}
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class HiDreamO1TE(torch.nn.Module):
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"""Passthrough TE: emits int token ids; the Qwen3-VL backbone in diffusion_model does the actual encoding."""
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def __init__(self, device="cpu", dtype=None, model_options={}):
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super().__init__()
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self.dtypes = {torch.float32}
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self.disable_offload = True # skips dynamic VRAM management for this zero-parameter module
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self.device = torch.device("cpu") if device is None else torch.device(device)
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def encode_token_weights(self, token_weight_pairs):
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tok_pairs = token_weight_pairs["hidream_o1"][0]
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ids = [int(t[0]) for t in tok_pairs]
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input_ids = torch.tensor([ids], dtype=torch.long)
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# Surrogate keeps the cross_attn slot non-empty for CONDITIONING
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# plumbing; the model reads text_input_ids out of `extra` instead.
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cross_attn = input_ids.unsqueeze(-1).to(torch.float32)
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extra = {"text_input_ids": input_ids}
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return cross_attn, None, extra
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def load_sd(self, sd):
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return []
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def get_sd(self):
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return {}
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def reset_clip_options(self):
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pass
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def set_clip_options(self, options):
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pass
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