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@@ -33,3 +33,52 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+models/H100/ltx-2.3-22b-distilled-fp8-t2v_lora/timing_cache.tc filter=lfs diff=lfs merge=lfs -text
+models/H100/ltx-2.3-22b-distilled-fp8-t2v_lora/transformer_blocks.0.qlip filter=lfs diff=lfs merge=lfs -text
+models/H100/ltx-2.3-22b-distilled-fp8-t2v_lora/transformer_blocks.1.qlip filter=lfs diff=lfs merge=lfs -text
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models/H100/ltx-2.3-22b-distilled-fp8-t2v_lora/config.json

@@ -0,0 +1,7 @@
+{
+  "model_type": "LTXAVModel",
+  "backend": "nvidia",
+  "base_model_name": "model",
+  "format": "qlip",
+  "strip_weights": false
+}

models/H100/ltx-2.3-22b-distilled-fp8-t2v_lora/lora_config.json

@@ -0,0 +1,62 @@
+{
+  "configs": [
+    {
+      "name": "auto_transformer_blocks",
+      "block_prefix": "transformer_blocks",
+      "num_blocks": 48,
+      "max_features": 16384,
+      "layers": [
+        {
+          "name": "attn1.to_k",
+          "out_features": 4096,
+          "in_features": 4096
+        },
+        {
+          "name": "attn1.to_out.0",
+          "out_features": 4096,
+          "in_features": 4096
+        },
+        {
+          "name": "attn1.to_q",
+          "out_features": 4096,
+          "in_features": 4096
+        },
+        {
+          "name": "attn1.to_v",
+          "out_features": 4096,
+          "in_features": 4096
+        },
+        {
+          "name": "attn2.to_k",
+          "out_features": 4096,
+          "in_features": 4096
+        },
+        {
+          "name": "attn2.to_out.0",
+          "out_features": 4096,
+          "in_features": 4096
+        },
+        {
+          "name": "attn2.to_q",
+          "out_features": 4096,
+          "in_features": 4096
+        },
+        {
+          "name": "attn2.to_v",
+          "out_features": 4096,
+          "in_features": 4096
+        },
+        {
+          "name": "ff.net.0.proj",
+          "out_features": 16384,
+          "in_features": 4096
+        },
+        {
+          "name": "ff.net.2",
+          "out_features": 4096,
+          "in_features": 16384
+        }
+      ]
+    }
+  ]
+}

models/H100/ltx-2.3-22b-distilled-fp8-t2v_lora/qlip_key.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2dadde25479db3f218597c64a2e5fb2f46afd463d7d9faef4ba08c2e68f09241
+size 32

models/H100/ltx-2.3-22b-distilled-fp8-t2v_lora/qlip_patch.py

@@ -0,0 +1,311 @@
+"""LTX-2.3 (LTXAV) inference patches for ComfyUI-Qlip.
+
+Place this file next to the compiled engines (same directory as .qlip/.engine files).
+ComfyUI-Qlip's engine_loader.py will auto-load it and call:
+  - patch_signatures(dm) BEFORE auto_setup()
+  - patch_caller(dm)     AFTER engine loading
+
+Why these patches are needed:
+  - Compiled blocks expect stacked pe tensors [2, ...] instead of (cos, sin, bool) tuples
+    because qlip strips tuples containing non-tensor elements (the bool split_mode).
+  - Block split_mode flags are stored as attributes (constant per generation).
+  - _prepare_timestep returns CompressedTimestep objects → expand to raw tensors.
+  - _process_transformer_blocks must stack pe before passing to compiled blocks.
+  - LTX-2.3 adds v_prompt_timestep / a_prompt_timestep for cross_attention_adaln.
+"""
+
+import inspect
+import types
+import torch
+
+
+def patch_caller(dm):
+    """All LTX-2.3 inference patches. Called AFTER engine loading.
+
+    At inference time, blocks are already CompiledModules — their forward
+    signatures come from the compiled engine and cannot be changed. All three
+    patches work at the transformer (caller) level:
+
+    1. _prepare_timestep — expand CompressedTimestep → raw tensors
+    2. block.forward wrapper — unstack pe tensors back to (cos, sin, bool) tuples
+       before calling the CompiledModule (which expects tuples internally)
+    3. _process_transformer_blocks — stack pe tuples into [2,...] tensors
+       before passing to blocks (matches compiled engine input format)
+    """
+    _patch_compressed_timestep(dm)
+    _patch_blocks_pe_to_stacked(dm)
+    _patch_process_transformer_blocks(dm)
+
+
+# ---------------------------------------------------------------
+# patch_compressed_timestep
+# ---------------------------------------------------------------
+
+def _patch_compressed_timestep(transformer):
+    orig_prepare = transformer._prepare_timestep
+
+    def patched_prepare_timestep(timestep, batch_size, hidden_dtype, **kwargs):
+        result = orig_prepare(timestep, batch_size, hidden_dtype, **kwargs)
+
+        from comfy.ldm.lightricks.av_model import CompressedTimestep
+
+        def expand_if_compressed(obj):
+            if isinstance(obj, CompressedTimestep):
+                return obj.expand()
+            return obj
+
+        # Handle both 2-tuple (old) and 3+ tuple (new) return formats
+        if len(result) == 2:
+            timesteps, embedded = result
+            extra = ()
+        else:
+            timesteps, embedded = result[0], result[1]
+            extra = tuple(result[2:])
+
+        timesteps_raw = []
+        for item in timesteps:
+            if isinstance(item, list):
+                timesteps_raw.append([expand_if_compressed(x) for x in item])
+            else:
+                timesteps_raw.append(expand_if_compressed(item))
+
+        embedded_raw = [expand_if_compressed(e) for e in embedded]
+
+        extra_raw = tuple(
+            expand_if_compressed(x) if not isinstance(x, (list, tuple))
+            else [expand_if_compressed(i) for i in x]
+            for x in extra
+        )
+
+        return (timesteps_raw, embedded_raw) + extra_raw
+
+    transformer._prepare_timestep = patched_prepare_timestep
+    print("[ltx23_patch] Patched _prepare_timestep: CompressedTimestep → raw tensors")
+
+
+# ---------------------------------------------------------------
+# patch_blocks_pe_to_stacked
+# ---------------------------------------------------------------
+
+def _patch_blocks_pe_to_stacked(transformer):
+    """Patch block.forward to accept stacked [2,...] pe tensors."""
+    # Check if blocks have lora_packed (they do if compiled with LoRA)
+    def _block_has_lora(b):
+        target = b.model if hasattr(b, "model") else b
+        try:
+            sig = getattr(target.forward, "__signature__", None) or inspect.signature(target.forward)
+            return "lora_packed" in sig.parameters
+        except Exception:
+            return False
+
+    has_lora = any(_block_has_lora(b) for b in transformer.transformer_blocks)
+
+    for block in transformer.transformer_blocks:
+        target = block.model if hasattr(block, "model") else block
+        target._v_split_pe = None
+        target._a_split_pe = None
+        target._v_cross_split_pe = None
+        target._a_cross_split_pe = None
+
+        orig_forward = target.forward
+
+        def make_patched_forward(blk, orig_fwd, with_lora):
+            def _core(v_pe, a_pe, v_cross_pe, a_cross_pe, transformer_options):
+                if transformer_options is None:
+                    transformer_options = {}
+
+                def unstack(stacked, split_mode):
+                    if stacked is None:
+                        return None
+                    return (stacked[0], stacked[1], split_mode)
+
+                return (
+                    unstack(v_pe, blk._v_split_pe),
+                    unstack(a_pe, blk._a_split_pe),
+                    unstack(v_cross_pe, blk._v_cross_split_pe),
+                    unstack(a_cross_pe, blk._a_cross_split_pe),
+                    transformer_options,
+                )
+
+            if with_lora:
+                def patched_forward(
+                    lora_packed,
+                    x, v_context=None, a_context=None, attention_mask=None,
+                    v_timestep=None, a_timestep=None,
+                    v_pe=None, a_pe=None, v_cross_pe=None, a_cross_pe=None,
+                    v_cross_scale_shift_timestep=None, a_cross_scale_shift_timestep=None,
+                    v_cross_gate_timestep=None, a_cross_gate_timestep=None,
+                    v_prompt_timestep=None, a_prompt_timestep=None,
+                    transformer_options=None,
+                ):
+                    vp, ap, vcp, acp, to = _core(v_pe, a_pe, v_cross_pe, a_cross_pe, transformer_options)
+                    return orig_fwd(
+                        lora_packed, x, v_context=v_context, a_context=a_context,
+                        attention_mask=attention_mask,
+                        v_timestep=v_timestep, a_timestep=a_timestep,
+                        v_pe=vp, a_pe=ap, v_cross_pe=vcp, a_cross_pe=acp,
+                        v_cross_scale_shift_timestep=v_cross_scale_shift_timestep,
+                        a_cross_scale_shift_timestep=a_cross_scale_shift_timestep,
+                        v_cross_gate_timestep=v_cross_gate_timestep,
+                        a_cross_gate_timestep=a_cross_gate_timestep,
+                        v_prompt_timestep=v_prompt_timestep,
+                        a_prompt_timestep=a_prompt_timestep,
+                        transformer_options=to,
+                    )
+            else:
+                def patched_forward(
+                    x, v_context=None, a_context=None, attention_mask=None,
+                    v_timestep=None, a_timestep=None,
+                    v_pe=None, a_pe=None, v_cross_pe=None, a_cross_pe=None,
+                    v_cross_scale_shift_timestep=None, a_cross_scale_shift_timestep=None,
+                    v_cross_gate_timestep=None, a_cross_gate_timestep=None,
+                    v_prompt_timestep=None, a_prompt_timestep=None,
+                    transformer_options=None,
+                ):
+                    vp, ap, vcp, acp, to = _core(v_pe, a_pe, v_cross_pe, a_cross_pe, transformer_options)
+                    return orig_fwd(
+                        x, v_context=v_context, a_context=a_context,
+                        attention_mask=attention_mask,
+                        v_timestep=v_timestep, a_timestep=a_timestep,
+                        v_pe=vp, a_pe=ap, v_cross_pe=vcp, a_cross_pe=acp,
+                        v_cross_scale_shift_timestep=v_cross_scale_shift_timestep,
+                        a_cross_scale_shift_timestep=a_cross_scale_shift_timestep,
+                        v_cross_gate_timestep=v_cross_gate_timestep,
+                        a_cross_gate_timestep=a_cross_gate_timestep,
+                        v_prompt_timestep=v_prompt_timestep,
+                        a_prompt_timestep=a_prompt_timestep,
+                        transformer_options=to,
+                    )
+            return patched_forward
+
+        target.forward = make_patched_forward(target, orig_forward, has_lora)
+
+    print(f"[ltx23_patch] Patched {len(transformer.transformer_blocks)} block forwards "
+          f"(lora={has_lora})")
+
+
+# ---------------------------------------------------------------
+# patch_process_transformer_blocks
+# ---------------------------------------------------------------
+
+def _patch_process_transformer_blocks(transformer):
+    """Patch _process_transformer_blocks to stack pe tuples before calling blocks."""
+
+    def patched_process_transformer_blocks(
+        self_tr, x, context, attention_mask, timestep, pe,
+        transformer_options={}, **kwargs,
+    ):
+        (v_pe_tuple, v_cross_tuple) = pe[0]
+        (a_pe_tuple, a_cross_tuple) = pe[1]
+
+        def stack_and_store(pe_tuple):
+            cos, sin, split_mode = pe_tuple
+            return torch.stack([cos, sin]), split_mode
+
+        v_pe_stacked, v_split = stack_and_store(v_pe_tuple)
+        v_cross_stacked, v_cross_split = stack_and_store(v_cross_tuple)
+        a_pe_stacked, a_split = stack_and_store(a_pe_tuple)
+        a_cross_stacked, a_cross_split = stack_and_store(a_cross_tuple)
+
+        # Store split_mode on all blocks
+        for block in self_tr.transformer_blocks:
+            blk = block.model if hasattr(block, "model") else block
+            blk._v_split_pe = v_split
+            blk._a_split_pe = a_split
+            blk._v_cross_split_pe = v_cross_split
+            blk._a_cross_split_pe = a_cross_split
+
+        vx = x[0]
+        ax = x[1]
+        v_context_t = context[0]
+        a_context_t = context[1]
+        v_timestep_t = timestep[0]
+        a_timestep_t = timestep[1]
+        (
+            av_ca_audio_scale_shift_timestep,
+            av_ca_video_scale_shift_timestep,
+            av_ca_a2v_gate_noise_timestep,
+            av_ca_v2a_gate_noise_timestep,
+        ) = timestep[2]
+        # LTX-2.3: prompt_timestep for cross_attention_adaln
+        v_prompt_timestep_t = timestep[3] if len(timestep) > 3 else None
+        a_prompt_timestep_t = timestep[4] if len(timestep) > 4 else None
+
+        patches_replace = transformer_options.get("patches_replace", {})
+        blocks_replace = patches_replace.get("dit", {})
+
+        for i, block in enumerate(self_tr.transformer_blocks):
+            if ("double_block", i) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["img"] = block(
+                        args["img"],
+                        v_context=args["v_context"],
+                        a_context=args["a_context"],
+                        attention_mask=args["attention_mask"],
+                        v_timestep=args["v_timestep"],
+                        a_timestep=args["a_timestep"],
+                        v_pe=args["v_pe"],
+                        a_pe=args["a_pe"],
+                        v_cross_pe=args["v_cross_pe"],
+                        a_cross_pe=args["a_cross_pe"],
+                        v_cross_scale_shift_timestep=args["v_cross_scale_shift_timestep"],
+                        a_cross_scale_shift_timestep=args["a_cross_scale_shift_timestep"],
+                        v_cross_gate_timestep=args["v_cross_gate_timestep"],
+                        a_cross_gate_timestep=args["a_cross_gate_timestep"],
+                        v_prompt_timestep=args.get("v_prompt_timestep"),
+                        a_prompt_timestep=args.get("a_prompt_timestep"),
+                        transformer_options=args["transformer_options"],
+                    )
+                    return out
+
+                out = blocks_replace[("double_block", i)](
+                    {
+                        "img": (vx, ax),
+                        "v_context": v_context_t,
+                        "a_context": a_context_t,
+                        "attention_mask": attention_mask,
+                        "v_timestep": v_timestep_t,
+                        "a_timestep": a_timestep_t,
+                        "v_pe": v_pe_stacked,
+                        "a_pe": a_pe_stacked,
+                        "v_cross_pe": v_cross_stacked,
+                        "a_cross_pe": a_cross_stacked,
+                        "v_cross_scale_shift_timestep": av_ca_video_scale_shift_timestep,
+                        "a_cross_scale_shift_timestep": av_ca_audio_scale_shift_timestep,
+                        "v_cross_gate_timestep": av_ca_a2v_gate_noise_timestep,
+                        "a_cross_gate_timestep": av_ca_v2a_gate_noise_timestep,
+                        "v_prompt_timestep": v_prompt_timestep_t,
+                        "a_prompt_timestep": a_prompt_timestep_t,
+                        "transformer_options": transformer_options,
+                    },
+                    {"original_block": block_wrap},
+                )
+                vx, ax = out["img"]
+            else:
+                vx, ax = block(
+                    (vx, ax),
+                    v_context=v_context_t,
+                    a_context=a_context_t,
+                    attention_mask=attention_mask,
+                    v_timestep=v_timestep_t,
+                    a_timestep=a_timestep_t,
+                    v_pe=v_pe_stacked,
+                    a_pe=a_pe_stacked,
+                    v_cross_pe=v_cross_stacked,
+                    a_cross_pe=a_cross_stacked,
+                    v_cross_scale_shift_timestep=av_ca_video_scale_shift_timestep,
+                    a_cross_scale_shift_timestep=av_ca_audio_scale_shift_timestep,
+                    v_cross_gate_timestep=av_ca_a2v_gate_noise_timestep,
+                    a_cross_gate_timestep=av_ca_v2a_gate_noise_timestep,
+                    v_prompt_timestep=v_prompt_timestep_t,
+                    a_prompt_timestep=a_prompt_timestep_t,
+                    transformer_options=transformer_options,
+                )
+
+        return [vx, ax]
+
+    transformer._process_transformer_blocks = types.MethodType(
+        patched_process_transformer_blocks, transformer,
+    )
+    print("[ltx23_patch] Patched _process_transformer_blocks: stack pe → [2,...] tensors")

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