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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
42 lines
2.0 KiB
Python
42 lines
2.0 KiB
Python
"""HiDream-O1 two-pass attention: tokens [0, ar_len) are causal, [ar_len, T)
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attend full K/V. Splitting Q at the boundary avoids the (B, 1, T, T) additive
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mask the general-purpose path would build (~500 MB at T~16K) and lets the
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gen half hit the user's preferred backend via optimized_attention.
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"""
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import torch
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import comfy.ops
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from comfy.ldm.modules.attention import optimized_attention
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def make_two_pass_attention(ar_len: int, transformer_options=None):
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"""Build a two-pass attention callable. AR pass uses SDPA-causal directly, gen pass routes through optimized_attention.
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The AR pass goes through SDPA directand bypasses wrappers, it is only ~1% of T at typical edit sizes.
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"""
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def two_pass_attention(q, k, v, heads, **kwargs):
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B, H, T, D = q.shape
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if T < k.shape[2]: # KV-cache hot path: Q is shorter than K/V (cached AR prefix is in K/V only), all fresh Q positions are in the gen region, single full-attention call
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out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options)
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elif ar_len >= T:
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out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=True)
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elif ar_len <= 0:
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out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options)
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else:
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out_ar = comfy.ops.scaled_dot_product_attention(
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q[:, :, :ar_len], k[:, :, :ar_len], v[:, :, :ar_len],
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attn_mask=None, dropout_p=0.0, is_causal=True,
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)
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out_gen = optimized_attention(
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q[:, :, ar_len:], k, v, heads,
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mask=None, skip_reshape=True, skip_output_reshape=True,
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transformer_options=transformer_options,
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)
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out = torch.cat([out_ar, out_gen], dim=2)
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return out.transpose(1, 2).reshape(B, T, H * D)
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return two_pass_attention
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