Duplicate from Phr00t/LTX2-Rapid-Merges

Co-authored-by: Phr00t <Phr00t@users.noreply.huggingface.co>

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+      "title": "First Image",
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "example.png",
+        "image"
+      ]
+    },
+    {
+      "id": 35,
+      "type": "LoadImage",
+      "pos": [
+        -86.44536100179037,
+        -298.3175231515558
+      ],
+      "size": [
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+        444.4890226581098
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+      "flags": {},
+      "order": 20,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            326
+          ]
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "title": "Last Image",
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "example.png",
+        "image"
+      ]
+    },
+    {
+      "id": 102,
+      "type": "LTXVAddGuideMulti",
+      "pos": [
+        711.0017742609549,
+        -301.90487422000064
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+      "size": [
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+      "flags": {},
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+        {
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+        {
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+          "label": "image_1",
+          "name": "num_guides.image_1",
+          "type": "IMAGE",
+          "link": 429
+        },
+        {
+          "name": "num_guides.strength_1",
+          "type": "FLOAT",
+          "widget": {
+            "name": "num_guides.strength_1"
+          },
+          "link": 238
+        },
+        {
+          "label": "image_2",
+          "name": "num_guides.image_2",
+          "type": "IMAGE",
+          "link": 543
+        }
+      ],
+      "outputs": [
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "links": [
+            283,
+            479
+          ]
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "links": [
+            284
+          ]
+        },
+        {
+          "name": "latent",
+          "type": "LATENT",
+          "links": [
+            236
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LTXVAddGuideMulti"
+      },
+      "widgets_values": [
+        "2",
+        0,
+        1,
+        -1,
+        1
+      ]
+    },
+    {
+      "id": 37,
+      "type": "LTXVPreprocess",
+      "pos": [
+        366.4490642616451,
+        -124.14229413462802
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+      "size": [
+        270,
+        58
+      ],
+      "flags": {},
+      "order": 35,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 78
+        },
+        {
+          "name": "img_compression",
+          "type": "INT",
+          "widget": {
+            "name": "img_compression"
+          },
+          "link": 167
+        }
+      ],
+      "outputs": [
+        {
+          "name": "output_image",
+          "type": "IMAGE",
+          "links": [
+            543
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LTXVPreprocess"
+      },
+      "widgets_values": [
+        33
+      ]
+    },
+    {
+      "id": 140,
+      "type": "Note",
+      "pos": [
+        -842.2026639749197,
+        -316.4639047325439
+      ],
+      "size": [
+        687.0133900544962,
+        493.0743919786187
+      ],
+      "flags": {},
+      "order": 21,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "This workflow is messy as shit, but LTX kinda requires that.\n\nMake sure all of these nodes are installed and updated:\nComfy-KJNodes (LTX improvements)\nComfy-LTXVideo (core nodes)\nVideoHelperSuite (core video)\nFill-Nodes (simplifies config + fast interpolation)\n\nDefaults are \"first to last\" generation. Bypass the \"Last Image\" to the right and set LTXVAddGuideMulti \"Number of Guides\" to 1 if just doing I2V \"First Frame\".\n\nBypass the whole top group for T2V.\n\nModify stuff below to specify what kind of video you want to generate.\n\nYou shouldn't need to touch anything in the middle blue section, which contains all of the best settings and optimizations I could find.\n\nThe audio concat stuff is to support \"ping pong\" video output to add audio and double video length for looping purposes (better than silence imho).\n\nIf you have motion issues, try increasing the frame rate (to like 30 or higher). Also try disabling/bypassing RIFE interpolation which could contribute to very high motion artifacts too.\n\nThe \"ImageFrameSelect\" config can help cut off strange frames near the end if its not using your \"last frame\" correctly. This hasn't really been an issue lately, but I'll keep it there for now.\n\nGood luck, this model is pretty neat."
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    }
+  ],
+  "links": [
+    [
+      22,
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+    [
+      543,
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+      0,
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+      "IMAGE"
+    ]
+  ],
+  "groups": [
+    {
+      "id": 1,
+      "title": "I2V / First to Last Frame (Bypass if doing T2V)",
+      "bounding": [
+        -118.55839617373792,
+        -846.3134792458198,
+        1175.1397966081684,
+        1027.4161608638976
+      ],
+      "color": "#8A8",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 2,
+      "title": "Configuration & Prompt",
+      "bounding": [
+        -894.1455674446069,
+        212.2923672604866,
+        739.2019582499095,
+        1039.8355479558713
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+      "color": "#8A8",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 3,
+      "title": "Brains / Model Sampling (Shouldn't need to touch anything in here)",
+      "bounding": [
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+        209.64992140416265,
+        1267.7980404416276,
+        1159.5499763641924
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    }
+  ],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.8954302432552388,
+      "offset": [
+        903.9633351276846,
+        -412.35713990599004
+      ]
+    },
+    "workflowRendererVersion": "LG",
+    "ue_links": [],
+    "frontendVersion": "1.37.11",
+    "VHS_latentpreview": false,
+    "VHS_latentpreviewrate": 0,
+    "VHS_MetadataImage": true,
+    "VHS_KeepIntermediate": true
+  },
+  "version": 0.4
+}

MergingScript/fancy-apply.py

@@ -0,0 +1,381 @@
+#!/usr/bin/env python3
+
+# THIS IS FOR ADVANCED LORA INTO BASE MODEL MERGING
+# Designed for use with the LTX2 model
+#
+# Make a text file with a list of LORAs you want to merge in this format for each line:
+# <path to safetensors>,<strength>,<lerp>
+#
+# The "lerp" parameter means "how much should I overwrite tensors that compete with LORAs listed above?". A value of "0" just mixes them all together, while "1" hard applies the LORA delta
+#
+# You can also supply a separate audio and video strengths like this:
+# <path to safetensors>,<video strength>,<lerp>,<audio strength>
+#
+# Use this script like this:
+# python fancy-apply.py <base model safetensors> <lora list txt file> <merged output filename>
+
+import argparse
+import os
+from typing import Dict, Tuple, List, Optional
+from collections import defaultdict
+import math
+
+import torch
+from safetensors.torch import load_file, save_file
+from safetensors import safe_open
+
+
+# ----------------- Tuning knobs ----------------- #
+
+# If True, the normalized component uses:
+#   scale_norm = eff_strength / max(1.0, sum_eff_strengths_for_key)
+NORMALIZE_OVERLAPS = True
+
+# Per‑LoRA clipping threshold:
+# If not None, each LoRA's delta is clipped so that:
+#   ||delta|| <= CLIP_RATIO * ||W||
+CLIP_RATIO: Optional[float] = 1.0
+
+
+# ----------------- Parsing LoRA list ----------------- #
+
+def parse_lora_list(path: str) -> List[Tuple[str, float, float, float]]:
+    """
+    Parse list_of_loras.txt with lines like:
+      filename.safetensors,0.7,0.0
+      filename2.safetensors,1.0,0.5,0.3
+
+    Returns list of tuples:
+      (path, video_strength, lerp_with_existing, audio_strength)
+
+    Where:
+      video_strength: base strength for video/shared weights
+      audio_strength: base strength for audio weights
+                      (defaults to video_strength if omitted)
+      lerp_with_existing in [0, 1]:
+        0.0 -> fully normalized
+        1.0 -> fully direct
+        between -> blend between normalized and direct
+    """
+    loras: List[Tuple[str, float, float, float]] = []
+    with open(path, "r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if not line or line.startswith("#"):
+                continue
+
+            parts = [p.strip() for p in line.split(",")]
+            if len(parts) < 3:
+                raise ValueError(f"Invalid LoRA line (need at least file,video_strength,lerp): {line}")
+
+            filename = parts[0]
+            video_strength = float(parts[1])
+            lerp = float(parts[2])
+
+            if len(parts) >= 4:
+                audio_strength = float(parts[3])
+            else:
+                audio_strength = video_strength
+
+            lerp = max(0.0, min(1.0, lerp))
+
+            loras.append((filename, video_strength, lerp, audio_strength))
+
+    return loras
+
+
+# ----------------- Base loading ----------------- #
+
+def load_base_with_metadata(path: str):
+    with safe_open(path, framework="pt", device="cpu") as f:
+        metadata = f.metadata() or {}
+    tensors = load_file(path, device="cpu")
+    return tensors, metadata
+
+
+# ----------------- LoRA key grouping ----------------- #
+
+def group_lora_pairs(lora_tensors: Dict[str, torch.Tensor]):
+    prefixes = {}
+    for k in lora_tensors.keys():
+        if k.endswith(".lora_A.weight"):
+            prefix = k[: -len(".lora_A.weight")]
+            prefixes.setdefault(prefix, {})["A"] = k
+        elif k.endswith(".lora_B.weight"):
+            prefix = k[: -len(".lora_B.weight")]
+            prefixes.setdefault(prefix, {})["B"] = k
+        elif k.endswith(".alpha"):
+            prefix = k[: -len(".alpha")]
+            prefixes.setdefault(prefix, {})["alpha"] = k
+
+    for prefix, keys in prefixes.items():
+        if "A" not in keys or "B" not in keys:
+            print(f"Warning: incomplete LoRA prefix {prefix}")
+            continue
+        yield prefix, keys["A"], keys["B"], keys.get("alpha")
+
+
+def find_base_weight_key(base_tensors, lora_prefix):
+    candidates = [
+        f"{lora_prefix}.weight",
+        f"model.{lora_prefix}.weight",
+        lora_prefix,
+        f"model.{lora_prefix}",
+    ]
+    for c in candidates:
+        if c in base_tensors:
+            return c
+    return None
+
+
+# ----------------- Audio / video classification ----------------- #
+
+def classify_prefix(prefix: str) -> str:
+    """
+    Classify a LoRA prefix as 'audio', 'video', 'cross', or 'shared'.
+    """
+    p = prefix.lower()
+
+    # Cross-modal first
+    if "audio_to_video" in p or "video_to_audio" in p:
+        return "cross"
+
+    # Audio-specific
+    if "audio_attn" in p or "audio_ff" in p or ".audio_" in p:
+        return "audio"
+
+    # Video-specific (heuristic)
+    if "video_attn" in p or "video_ff" in p or ".video_" in p:
+        return "video"
+
+    # Default: shared (treated as video-strength)
+    return "shared"
+
+
+def effective_strength_for_prefix(
+    prefix: str,
+    video_strength: float,
+    audio_strength: float,
+) -> float:
+    kind = classify_prefix(prefix)
+    if kind == "audio":
+        return audio_strength
+    elif kind == "video":
+        return video_strength
+    elif kind == "cross":
+        # Blend strengths for cross-modal
+        return math.sqrt(max(video_strength, 0.0) * max(audio_strength, 0.0))
+    else:
+        # shared
+        return video_strength
+
+
+# ----------------- Pass 1: strength sums per key ----------------- #
+
+def compute_strength_sums(
+    base_tensors,
+    lora_specs: List[Tuple[str, float, float, float]],
+) -> Dict[str, float]:
+    """
+    For each base weight key, compute the sum of effective strengths of all LoRAs
+    that touch it (using video/audio/cross classification).
+    """
+    strength_sum: Dict[str, float] = defaultdict(float)
+
+    for lora_path, video_strength, lerp, audio_strength in lora_specs:
+        print(f"[Pass 1] Scanning {lora_path} (video={video_strength}, audio={audio_strength}, lerp={lerp})")
+        lora_tensors = load_file(lora_path, device="cpu")
+
+        for prefix, A_key, B_key, alpha_key in group_lora_pairs(lora_tensors):
+            base_key = find_base_weight_key(base_tensors, prefix)
+            if base_key is None:
+                continue
+
+            eff_strength = effective_strength_for_prefix(prefix, video_strength, audio_strength)
+            strength_sum[base_key] += eff_strength
+
+        del lora_tensors
+
+    print(f"[Pass 1] Keys with strength contributions: {len(strength_sum)}")
+    return strength_sum
+
+
+# ----------------- Pass 2: streaming application ----------------- #
+
+def apply_loras_streaming(
+    base_tensors,
+    lora_specs: List[Tuple[str, float, float, float]],
+    strength_sum: Dict[str, float],
+    clip_ratio: Optional[float] = CLIP_RATIO,
+):
+    for lora_path, video_strength, lerp, audio_strength in lora_specs:
+        print(f"[Pass 2] Applying {lora_path} (video={video_strength}, audio={audio_strength}, lerp={lerp})")
+        lora_tensors = load_file(lora_path, device="cpu")
+
+        applied = 0
+        skipped = 0
+
+        for prefix, A_key, B_key, alpha_key in group_lora_pairs(lora_tensors):
+            base_key = find_base_weight_key(base_tensors, prefix)
+            if base_key is None:
+                skipped += 1
+                continue
+
+            W = base_tensors[base_key]
+
+            A = lora_tensors[A_key].to(torch.float32)
+            B = lora_tensors[B_key].to(torch.float32)
+            delta = B @ A
+
+            if delta.shape != W.shape:
+                raise ValueError(
+                    f"Shape mismatch for {prefix}: delta {delta.shape} vs base {W.shape}"
+                )
+
+            rank = A.shape[0] if A.dim() == 2 else A.numel()
+
+            # Effective strength for this prefix (audio/video/cross/shared)
+            eff_strength = effective_strength_for_prefix(prefix, video_strength, audio_strength)
+
+            # Base strength + alpha scaling
+            if alpha_key is not None:
+                alpha = float(lora_tensors[alpha_key].to(torch.float32).item())
+                base_scale = eff_strength * alpha / max(rank, 1)
+            else:
+                base_scale = eff_strength
+
+            # Weighted normalization
+            if NORMALIZE_OVERLAPS:
+                total_strength = strength_sum.get(base_key, 0.0)
+                denom = max(1.0, total_strength)
+                scale_norm = base_scale / denom
+            else:
+                scale_norm = base_scale
+
+            # Direct (unnormalized) component
+            scale_direct = base_scale
+
+            # LERP between normalized and direct
+            scale = (1.0 - lerp) * scale_norm + lerp * scale_direct
+
+            delta_scaled = delta * scale
+
+            # Per‑LoRA clipping
+            if clip_ratio is not None:
+                Wf = W.to(torch.float32)
+                base_norm = Wf.norm().item()
+                delta_norm = delta_scaled.norm().item()
+
+                if delta_norm > clip_ratio * base_norm and delta_norm > 0:
+                    delta_scaled *= (clip_ratio * base_norm) / delta_norm
+
+            # Apply update
+            W_new = W.to(torch.float32) + delta_scaled
+            base_tensors[base_key] = W_new.to(W.dtype)
+
+            applied += 1
+
+        print(f"[Pass 2] {lora_path}: applied {applied}, skipped {skipped}")
+        del lora_tensors
+
+
+def apply_loras_to_base(base_tensors, lora_specs):
+    strength_sum = compute_strength_sums(base_tensors, lora_specs)
+    apply_loras_streaming(base_tensors, lora_specs, strength_sum)
+
+
+# ----------------- FP8 conversion ----------------- #
+
+def is_vae_key(key: str) -> bool:
+    return any(key.startswith(p) for p in [
+        "first_stage_model.",
+        "model.first_stage_model.",
+        "vae.",
+        "model.vae.",
+    ])
+
+
+def is_text_encoder_key(key: str) -> bool:
+    return any(key.startswith(p) for p in [
+        "text_encoder.",
+        "model.text_encoder.",
+        "cond_stage_model.",
+        "model.cond_stage_model.",
+    ])
+
+
+def is_unet_key(key: str) -> bool:
+    return any(key.startswith(p) for p in [
+        "model.diffusion_model.",
+        "diffusion_model.",
+    ])
+
+
+def convert_to_fp8_inplace(tensors: Dict[str, torch.Tensor]):
+    fp8_dtype = torch.float8_e4m3fn
+
+    converted = 0
+    skipped_vae = 0
+    skipped_other = 0
+
+    for k, v in list(tensors.items()):
+        if not torch.is_floating_point(v):
+            skipped_other += 1
+            continue
+
+        if is_vae_key(k):
+            skipped_vae += 1
+            continue
+
+        if is_unet_key(k) or is_text_encoder_key(k):
+            tensors[k] = v.to(fp8_dtype)
+            converted += 1
+        else:
+            skipped_other += 1
+
+    print(
+        f"FP8 conversion: converted={converted}, "
+        f"skipped_vae={skipped_vae}, skipped_other={skipped_other}"
+    )
+
+
+# ----------------- Main CLI ----------------- #
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=(
+            "Apply LTX2-style LoRAs with separate video/audio strengths, "
+            "strength-weighted normalization, LERP blending, per‑LoRA clipping, "
+            "FP8 conversion, and metadata preservation (streaming, memory‑efficient)."
+        )
+    )
+    parser.add_argument("base", help="Base checkpoint (.safetensors)")
+    parser.add_argument("lora_list", help="Text file: path,video_strength,lerp[,audio_strength]")
+    parser.add_argument("output", help="Output FP8 checkpoint (.safetensors)")
+
+    args = parser.parse_args()
+
+    if not os.path.isfile(args.base):
+        raise FileNotFoundError(args.base)
+
+    lora_specs = parse_lora_list(args.lora_list)
+    if not lora_specs:
+        raise ValueError("No LoRAs specified.")
+
+    print(f"Loading base checkpoint: {args.base}")
+    base_tensors, metadata = load_base_with_metadata(args.base)
+    print(f"Base checkpoint has {len(base_tensors)} tensors.")
+
+    apply_loras_to_base(base_tensors, lora_specs)
+
+    print("Converting UNet + text encoder to FP8 (leaving VAE untouched)...")
+    convert_to_fp8_inplace(base_tensors)
+
+    print(f"Saving merged FP8 checkpoint to: {args.output}")
+    os.makedirs(os.path.dirname(args.output) or ".", exist_ok=True)
+    save_file(base_tensors, args.output, metadata=metadata)
+    print("Done.")
+
+
+if __name__ == "__main__":
+    main()

README.md

@@ -0,0 +1,39 @@
+---
+license: other
+license_name: ltx-2-community-license-agreement
+license_link: https://huggingface.co/Lightricks/LTX-2/raw/main/LICENSE
+base_model:
+- Lightricks/LTX-2
+pipeline_tag: image-text-to-video
+tags:
+- ltx2
+- t2v
+- i2v
+---
+**I'm winding down maintaining these models. I recommend checking out the [merging script](https://huggingface.co/Phr00t/LTX2-Rapid-Merges/blob/main/MergingScript/fancy-apply.py) to create your own, as at least the NSFW model is very outdated. Thank you for all of the support and feedback!**
+
+These are experimental FP8 merges of the LTX2 diffusion model. "All in ones" do not seem to be in the cards, as LTX2 has lots of (large) moving parts, including 2 VAEs for video and audio. So, for the meantime, I'm just going to focus on the diffusion model. For all other models needed to run LTX2, please refer here: https://huggingface.co/Kijai/LTXV2_comfy
+
+**Workflow uploaded!** It is messy as shit but does "everything" (I2V, First to Last Frame and T2V) very quickly and efficiently using all the tricks I've collected. I highly recommend using it and read the included note (particularly about required nodes). "Save output" is set to "false" on the VideoCombine, so switch that to "true" when you start making something you want to keep.
+
+The text encoder can be found here (I recommend fp8-scaled): https://huggingface.co/Comfy-Org/ltx-2/tree/main/split_files/text_encoders
+
+**I can only recommend my workflow with the included settings, as there are so many pitfalls getting LTX to work properly**. Old models have been removed because they were generated from a broken script. All models now should be "working" moving forward.
+
+**----- SFW VERSION HISTORY -----**
+
+**V3:** Distill LORA at 0.9 with the detailer LORA at 0.4. Good general purpose LTX2 model.
+
+**V4:** Small adjustment using an updated merging tool. Includes the distill LORA at 1.0 and detailer at 0.5, which works well with my workflow.
+
+**V5:** Now contains the I2V Adapter from https://huggingface.co/MachineDelusions/LTX-2_Image2Video_Adapter_LoRa. This seems to work just fine with T2V. I mixed only the video portion, leaving audio untouched (since the LORA degrades audio slightly otherwise).
+
+**----- NSFW VERSION HISTORY ----**
+
+**V6:** Contains every NSFW LORA I could find on CivitAI for LTX2 at varying weights at of 1/17/26. Fixed a major problem with these merges, and now this is actually a decent, legit first pass at a general purpose NSFW LTX2 diffusion model. Genitals overall are still weak, so I2V for best results. **THIS DOES NOT INCLUDE MY LATEST POVNSFW**
+
+**V41:** The version number reflects all of the internal iterations before deciding that this was "good enough" for release! This combines my POV NSFW LORA with lots of great work by CJ, TenStrip and other creators on CivitAI. A new merging method is used that handles overlaped LORA tensors individually to bring the best of the LORAs together. **USE WITH MY WORKFLOW**
+
+**V62:** Huge step up in quality from V41. Better order of NSFW LORAs. Includes the "Image2Video_Adapter_LoRa" for better I2V performance. Includes some new NSFW LORAs (but not my "pov v3 complete"). Significantly improves dialog and voice clarity (but not 100%). **Use with my workflow**
+
+Please support LORA creators and special thanks to the authors of these!

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