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fix(wan_video): replace ComfyUI/torch-MPS path with native mlx-video

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The ComfyUI path produced structurally-valid but visually garbage MP4s on
every job from 2026-07-17 onward while still reporting success.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
John King 2026-08-24 11:51:35 +10:00
parent 8e3085466d
commit bc8f5e7e23
3 changed files with 279 additions and 139 deletions
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4
server/operators/wan_video/manifest.json
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@ -1,8 +1,8 @@
{
"id": "wan_video",
"name": "Wan 2.2 Video (local, ComfyUI)",
"name": "Wan 2.2 Video (local, MLX)",
"category": "video-gen",
"description": "Prompt → MP4 (text-to-video), or image + prompt → MP4 (image-to-video), via Wan 2.2 TI2V-5B on the resident ComfyUI (Metal). 24fps, native 1280x704. Defaults are deliberately small (832x480, 49 frames ≈ 2s) — scale up once you know the node's speed. Weights: vendor/comfyui/models (wan2.2_ti2v_5B_fp16 + umt5_xxl_fp8 + wan2.2_vae, ~18GB).",
"description": "Prompt → MP4 (text-to-video), or image + prompt → MP4 (image-to-video), via Wan 2.2 TI2V-5B on mlx-video (native MLX/Metal). 24fps, native 1280x704. Defaults are deliberately small (832x480, 49 frames ≈ 2s ≈ 130s to render) — scale up once you know the node's speed. Weights: vendor/mlx-video-models/Wan2.2-TI2V-5B-mlx-q8 (~18GB, 8-bit transformer + bf16 UMT5 + VAE). Replaced the ComfyUI/PyTorch-MPS backend on 2026-08-02: that path was 1.6x slower and emitted garbage frames while reporting success.",
"accepts": ["image"],
"produces": ["video"],
"resources": "gpu",

238
server/operators/wan_video/run.py
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@ -1,25 +1,31 @@
"""Wan 2.2 TI2V-5B text/image→video via the resident ComfyUI (same pattern as
comfyui_sd: pure stdlib, ComfyUI's venv does the heavy lifting, model stays
cached in RAM between jobs). Frames come back as PNGs; ffmpeg muxes the MP4."""
"""Wan 2.2 TI2V-5B text/image→video via mlx-video (native MLX, Apple Silicon).
Replaces the previous ComfyUI/PyTorch-MPS path, which produced structurally-valid
but visually garbage MP4s on every job from 2026-07-17 onward and still reported
success. Benchmarked 2026-08-02 at identical settings (832x480, 49f, 20 steps,
cfg 5.0, seed 1234): MLX q8 132s and clean, ComfyUI fp16 216s and garbage.
Same operator id, same params, same output contract (outdir/wan_{seed}.mp4) — the
queue and any downstream consumer see no change. Weights are the q8 MLX conversion
in vendor/mlx-video-models; the generator holds them in unified memory itself, so
there is no resident-server handshake and no PYTORCH_MPS_HIGH_WATERMARK_RATIO
tuning (MLX manages its own allocation).
"""
import argparse
import json
import mimetypes
import os
import random
import subprocess
import sys
import time
import urllib.parse
import urllib.request
import uuid
from pathlib import Path
# job env has a minimal PATH; we shell out to ffmpeg for the mux
# job env has a minimal PATH; we shell out to ffprobe for output validation
os.environ["PATH"] = "/opt/homebrew/bin:/usr/local/bin:" + os.environ.get("PATH", "")
ROOT = Path(__file__).resolve().parents[3]
COMFY = ROOT / "vendor" / "comfyui"
API = "http://127.0.0.1:8188"
GEN = ROOT / "venvs" / "mlxvideo" / "bin" / "mlx_video.wan_2.generate"
MODEL_DIR = ROOT / "vendor" / "mlx-video-models" / "Wan2.2-TI2V-5B-mlx-q8"
FPS = 24 # Wan 2.2
ap = argparse.ArgumentParser()
@ -34,141 +40,101 @@ prompt = (p.get("prompt") or "").strip()
if not prompt:
print("ERROR: prompt is required")
sys.exit(1)
if not (COMFY / "main.py").exists():
print(f"ERROR: ComfyUI not installed at {COMFY}. Run scripts/install_comfyui.sh")
if not GEN.exists():
print(f"ERROR: mlx-video not installed at {GEN}. Run scripts/install_mlx_video.sh")
sys.exit(1)
model_file = COMFY / "models" / "diffusion_models" / "wan2.2_ti2v_5B_fp16.safetensors"
if not model_file.exists():
print("ERROR: Wan weights missing — see manifest description for the three files")
if not (MODEL_DIR / "model.safetensors").exists():
print(f"ERROR: MLX weights missing at {MODEL_DIR} — see manifest description")
sys.exit(1)
def alive():
try:
urllib.request.urlopen(f"{API}/", timeout=3)
return True
except Exception:
return False
def ensure_server():
if alive():
print("comfyui: already resident", flush=True)
return
print("comfyui: starting resident server ...", flush=True)
log = open("/tmp/comfyui.log", "ab")
subprocess.Popen(
[str(COMFY / ".venv" / "bin" / "python"), "main.py", "--port", "8188"],
cwd=str(COMFY), stdout=log, stderr=log, start_new_session=True)
for _ in range(90):
if alive():
print("comfyui: up", flush=True)
return
time.sleep(2)
print("ERROR: ComfyUI did not come up — see /tmp/comfyui.log")
sys.exit(1)
def upload_image(path):
"""POST multipart to /upload/image, return server-side filename."""
boundary = uuid.uuid4().hex
fname = Path(path).name
ctype = mimetypes.guess_type(fname)[0] or "application/octet-stream"
body = (f"--{boundary}\r\nContent-Disposition: form-data; name=\"image\"; "
f"filename=\"{fname}\"\r\nContent-Type: {ctype}\r\n\r\n").encode()
body += Path(path).read_bytes()
body += f"\r\n--{boundary}--\r\n".encode()
req = urllib.request.Request(f"{API}/upload/image", data=body, headers={
"Content-Type": f"multipart/form-data; boundary={boundary}"})
return json.load(urllib.request.urlopen(req))["name"]
def build(seed, start_image):
length = int(p.get("length", 49))
if length % 4 != 1:
length = int(p.get("length", 49))
if length % 4 != 1:
length = (length // 4) * 4 + 1 # model requires 4n+1 frames
lat = {"vae": ["v", 0], "width": int(p.get("width", 832)),
"height": int(p.get("height", 480)), "length": length, "batch_size": 1}
g = {
"u": {"class_type": "UNETLoader", "inputs": {
"unet_name": "wan2.2_ti2v_5B_fp16.safetensors", "weight_dtype": "default"}},
"c": {"class_type": "CLIPLoader", "inputs": {
"clip_name": "umt5_xxl_fp8_e4m3fn_scaled.safetensors", "type": "wan",
"device": "default"}},
"v": {"class_type": "VAELoader", "inputs": {"vae_name": "wan2.2_vae.safetensors"}},
"mo": {"class_type": "ModelSamplingSD3", "inputs": {"model": ["u", 0], "shift": 8.0}},
"pos": {"class_type": "CLIPTextEncode", "inputs": {"text": prompt, "clip": ["c", 0]}},
"neg": {"class_type": "CLIPTextEncode", "inputs": {
"text": p.get("negative", "blurry, distorted, low quality, static image, watermark, text"),
"clip": ["c", 0]}},
"lat": {"class_type": "Wan22ImageToVideoLatent", "inputs": lat},
"ks": {"class_type": "KSampler", "inputs": {
"seed": seed, "steps": int(p.get("steps", 20)), "cfg": float(p.get("cfg", 5.0)),
"sampler_name": "uni_pc", "scheduler": "simple", "denoise": 1.0,
"model": ["mo", 0], "positive": ["pos", 0], "negative": ["neg", 0],
"latent_image": ["lat", 0]}},
"dec": {"class_type": "VAEDecode", "inputs": {"samples": ["ks", 0], "vae": ["v", 0]}},
"sav": {"class_type": "SaveImage", "inputs": {
"filename_prefix": "mb_wan", "images": ["dec", 0]}},
}
if start_image:
g["img"] = {"class_type": "LoadImage", "inputs": {"image": start_image}}
lat["start_image"] = ["img", 0]
return g
ensure_server()
width, height = int(p.get("width", 832)), int(p.get("height", 480))
seed = int(p.get("seed", -1))
if seed < 0:
seed = random.randint(0, 2**31 - 1)
start_image = upload_image(a.input[0]) if a.input else None
print(f"seed={seed} mode={'i2v' if start_image else 't2v'} "
f"{p.get('width', 832)}x{p.get('height', 480)}x{p.get('length', 49)}f", flush=True)
body = json.dumps({"prompt": build(seed, start_image)}).encode()
req = urllib.request.Request(f"{API}/prompt", data=body,
headers={"Content-Type": "application/json"})
try:
pid = json.load(urllib.request.urlopen(req))["prompt_id"]
except urllib.error.HTTPError as e:
print(f"ERROR: ComfyUI rejected the workflow: {e.read().decode()[:600]}")
start_image = a.input[0] if a.input else None
if start_image and not Path(start_image).exists():
print(f"ERROR: input image not found: {start_image}")
sys.exit(1)
t0 = time.time()
images = []
while time.time() - t0 < 5400:
h = json.load(urllib.request.urlopen(f"{API}/history/{pid}"))
if pid in h:
st = h[pid].get("status", {})
if st.get("status_str") == "error":
print("ERROR: generation failed — check /tmp/comfyui.log")
print(json.dumps(st)[:400])
sys.exit(1)
if h[pid].get("outputs"):
for node in h[pid]["outputs"].values():
images += node.get("images", [])
break
time.sleep(5)
if not images:
print("ERROR: no frames produced (timeout after 90min)")
sys.exit(1)
import tempfile
frames_dir = Path(tempfile.mkdtemp(prefix="wan_frames_")) # outside outdir so frames don't register as assets
for i, im in enumerate(images):
q = urllib.parse.urlencode({"filename": im["filename"],
"subfolder": im.get("subfolder", ""),
"type": im.get("type", "output")})
(frames_dir / f"f{i:05d}.png").write_bytes(
urllib.request.urlopen(f"{API}/view?{q}").read())
out_mp4 = outdir / f"wan_{seed}.mp4"
r = subprocess.run(["ffmpeg", "-y", "-framerate", str(FPS),
"-i", str(frames_dir / "f%05d.png"),
"-c:v", "libx264", "-pix_fmt", "yuv420p", "-crf", "18",
str(out_mp4)], capture_output=True, text=True)
if r.returncode != 0 or not out_mp4.exists():
print("ERROR: ffmpeg mux failed:", r.stderr[-800:])
cmd = [
str(GEN),
"--model-dir", str(MODEL_DIR),
"--prompt", prompt,
"--negative-prompt", p.get(
"negative", "blurry, distorted, low quality, static image, watermark, text"),
"--width", str(width),
"--height", str(height),
"--num-frames", str(length),
"--steps", str(int(p.get("steps", 20))),
"--guide-scale", str(float(p.get("cfg", 5.0))),
"--seed", str(seed),
"--output-path", str(out_mp4),
]
if start_image:
cmd += ["--image", start_image]
print(f"seed={seed} mode={'i2v' if start_image else 't2v'} "
f"{width}x{height}x{length}f backend=mlx-q8", flush=True)
t0 = time.time()
r = subprocess.run(cmd, capture_output=True, text=True)
# NOTE: do not gate on the exit code alone. Under memory pressure the generator
# has been observed finishing the render, writing a complete and valid MP4, and
# only then being SIGKILLed during interpreter teardown (rc=-9, "leaked
# semaphore" warning). Judge the artefact on disk first; the exit code is only
# authoritative when there is nothing usable to inspect.
# Echo the generator's own stage timings (T5 / denoise / VAE) into the job log.
import re
ANSI = re.compile(r"\x1b\[[0-9;]*m") # generator colourises; job logs are plain text
for line in (r.stdout or "").splitlines():
s = ANSI.sub("", line).strip()
if any(k in s for k in ("T5 encoding", "Models loaded", "Denoising:", "VAE decode", "Total time")):
if "it/s" not in s and "s/it" not in s: # skip the tqdm bar
print(f" {s}", flush=True)
# --- output validation ---------------------------------------------------
# The old ComfyUI path marked six garbage jobs "done" because nothing here ever
# checked the result. These are structural checks only (a real content-quality
# check would need a perceptual model and would risk false failures on
# legitimately flat footage) — but they do catch the silent-empty-output case.
if not out_mp4.exists() or out_mp4.stat().st_size < 50_000:
print(f"ERROR: no usable video produced at {out_mp4}")
if r.returncode != 0:
print(f" mlx-video exited {r.returncode}")
print((r.stderr or r.stdout)[-1200:])
sys.exit(1)
print(f"done: {len(images)} frames → {out_mp4.name} in {time.time() - t0:.1f}s", flush=True)
probe = subprocess.run(
["ffprobe", "-v", "error", "-select_streams", "v:0", "-count_packets",
"-show_entries", "stream=width,height,nb_read_packets", "-of", "csv=p=0", str(out_mp4)],
capture_output=True, text=True)
if probe.returncode != 0:
print(f"ERROR: output is not a decodable video: {probe.stderr[-400:]}")
sys.exit(1)
try:
gw, gh, gframes = (int(x) for x in probe.stdout.strip().split(",")[:3])
except ValueError:
print(f"ERROR: could not parse ffprobe output: {probe.stdout!r}")
sys.exit(1)
if (gw, gh) != (width, height):
print(f"ERROR: dimension mismatch — asked {width}x{height}, got {gw}x{gh}")
sys.exit(1)
if gframes < length:
print(f"ERROR: truncated output — asked {length} frames, got {gframes}")
sys.exit(1)
if r.returncode != 0:
print(f"note: output validated OK despite generator exit {r.returncode} "
f"(killed at teardown, render was complete)", flush=True)
print(f"done: {gframes} frames @ {gw}x{gh} → {out_mp4.name} "
f"in {time.time() - t0:.1f}s", flush=True)

174
server/operators/wan_video/run.py.bak-comfyui-20260802-003510 Normal file
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@ -0,0 +1,174 @@
"""Wan 2.2 TI2V-5B text/image→video via the resident ComfyUI (same pattern as
comfyui_sd: pure stdlib, ComfyUI's venv does the heavy lifting, model stays
cached in RAM between jobs). Frames come back as PNGs; ffmpeg muxes the MP4."""
import argparse
import json
import mimetypes
import os
import random
import subprocess
import sys
import time
import urllib.parse
import urllib.request
import uuid
from pathlib import Path
# job env has a minimal PATH; we shell out to ffmpeg for the mux
os.environ["PATH"] = "/opt/homebrew/bin:/usr/local/bin:" + os.environ.get("PATH", "")
ROOT = Path(__file__).resolve().parents[3]
COMFY = ROOT / "vendor" / "comfyui"
API = "http://127.0.0.1:8188"
FPS = 24 # Wan 2.2
ap = argparse.ArgumentParser()
ap.add_argument("--input", action="append", default=[])
ap.add_argument("--outdir", required=True)
ap.add_argument("--params", default="{}")
a = ap.parse_args()
p = json.loads(a.params)
outdir = Path(a.outdir)
prompt = (p.get("prompt") or "").strip()
if not prompt:
print("ERROR: prompt is required")
sys.exit(1)
if not (COMFY / "main.py").exists():
print(f"ERROR: ComfyUI not installed at {COMFY}. Run scripts/install_comfyui.sh")
sys.exit(1)
model_file = COMFY / "models" / "diffusion_models" / "wan2.2_ti2v_5B_fp16.safetensors"
if not model_file.exists():
print("ERROR: Wan weights missing — see manifest description for the three files")
sys.exit(1)
def alive():
try:
urllib.request.urlopen(f"{API}/", timeout=3)
return True
except Exception:
return False
def ensure_server():
if alive():
print("comfyui: already resident", flush=True)
return
print("comfyui: starting resident server ...", flush=True)
log = open("/tmp/comfyui.log", "ab")
subprocess.Popen(
[str(COMFY / ".venv" / "bin" / "python"), "main.py", "--port", "8188"],
cwd=str(COMFY), stdout=log, stderr=log, start_new_session=True)
for _ in range(90):
if alive():
print("comfyui: up", flush=True)
return
time.sleep(2)
print("ERROR: ComfyUI did not come up — see /tmp/comfyui.log")
sys.exit(1)
def upload_image(path):
"""POST multipart to /upload/image, return server-side filename."""
boundary = uuid.uuid4().hex
fname = Path(path).name
ctype = mimetypes.guess_type(fname)[0] or "application/octet-stream"
body = (f"--{boundary}\r\nContent-Disposition: form-data; name=\"image\"; "
f"filename=\"{fname}\"\r\nContent-Type: {ctype}\r\n\r\n").encode()
body += Path(path).read_bytes()
body += f"\r\n--{boundary}--\r\n".encode()
req = urllib.request.Request(f"{API}/upload/image", data=body, headers={
"Content-Type": f"multipart/form-data; boundary={boundary}"})
return json.load(urllib.request.urlopen(req))["name"]
def build(seed, start_image):
length = int(p.get("length", 49))
if length % 4 != 1:
length = (length // 4) * 4 + 1 # model requires 4n+1 frames
lat = {"vae": ["v", 0], "width": int(p.get("width", 832)),
"height": int(p.get("height", 480)), "length": length, "batch_size": 1}
g = {
"u": {"class_type": "UNETLoader", "inputs": {
"unet_name": "wan2.2_ti2v_5B_fp16.safetensors", "weight_dtype": "default"}},
"c": {"class_type": "CLIPLoader", "inputs": {
"clip_name": "umt5_xxl_fp8_e4m3fn_scaled.safetensors", "type": "wan",
"device": "default"}},
"v": {"class_type": "VAELoader", "inputs": {"vae_name": "wan2.2_vae.safetensors"}},
"mo": {"class_type": "ModelSamplingSD3", "inputs": {"model": ["u", 0], "shift": 8.0}},
"pos": {"class_type": "CLIPTextEncode", "inputs": {"text": prompt, "clip": ["c", 0]}},
"neg": {"class_type": "CLIPTextEncode", "inputs": {
"text": p.get("negative", "blurry, distorted, low quality, static image, watermark, text"),
"clip": ["c", 0]}},
"lat": {"class_type": "Wan22ImageToVideoLatent", "inputs": lat},
"ks": {"class_type": "KSampler", "inputs": {
"seed": seed, "steps": int(p.get("steps", 20)), "cfg": float(p.get("cfg", 5.0)),
"sampler_name": "uni_pc", "scheduler": "simple", "denoise": 1.0,
"model": ["mo", 0], "positive": ["pos", 0], "negative": ["neg", 0],
"latent_image": ["lat", 0]}},
"dec": {"class_type": "VAEDecode", "inputs": {"samples": ["ks", 0], "vae": ["v", 0]}},
"sav": {"class_type": "SaveImage", "inputs": {
"filename_prefix": "mb_wan", "images": ["dec", 0]}},
}
if start_image:
g["img"] = {"class_type": "LoadImage", "inputs": {"image": start_image}}
lat["start_image"] = ["img", 0]
return g
ensure_server()
seed = int(p.get("seed", -1))
if seed < 0:
seed = random.randint(0, 2**31 - 1)
start_image = upload_image(a.input[0]) if a.input else None
print(f"seed={seed} mode={'i2v' if start_image else 't2v'} "
f"{p.get('width', 832)}x{p.get('height', 480)}x{p.get('length', 49)}f", flush=True)
body = json.dumps({"prompt": build(seed, start_image)}).encode()
req = urllib.request.Request(f"{API}/prompt", data=body,
headers={"Content-Type": "application/json"})
try:
pid = json.load(urllib.request.urlopen(req))["prompt_id"]
except urllib.error.HTTPError as e:
print(f"ERROR: ComfyUI rejected the workflow: {e.read().decode()[:600]}")
sys.exit(1)
t0 = time.time()
images = []
while time.time() - t0 < 5400:
h = json.load(urllib.request.urlopen(f"{API}/history/{pid}"))
if pid in h:
st = h[pid].get("status", {})
if st.get("status_str") == "error":
print("ERROR: generation failed — check /tmp/comfyui.log")
print(json.dumps(st)[:400])
sys.exit(1)
if h[pid].get("outputs"):
for node in h[pid]["outputs"].values():
images += node.get("images", [])
break
time.sleep(5)
if not images:
print("ERROR: no frames produced (timeout after 90min)")
sys.exit(1)
import tempfile
frames_dir = Path(tempfile.mkdtemp(prefix="wan_frames_")) # outside outdir so frames don't register as assets
for i, im in enumerate(images):
q = urllib.parse.urlencode({"filename": im["filename"],
"subfolder": im.get("subfolder", ""),
"type": im.get("type", "output")})
(frames_dir / f"f{i:05d}.png").write_bytes(
urllib.request.urlopen(f"{API}/view?{q}").read())
out_mp4 = outdir / f"wan_{seed}.mp4"
r = subprocess.run(["ffmpeg", "-y", "-framerate", str(FPS),
"-i", str(frames_dir / "f%05d.png"),
"-c:v", "libx264", "-pix_fmt", "yuv420p", "-crf", "18",
str(out_mp4)], capture_output=True, text=True)
if r.returncode != 0 or not out_mp4.exists():
print("ERROR: ffmpeg mux failed:", r.stderr[-800:])
sys.exit(1)
print(f"done: {len(images)} frames → {out_mp4.name} in {time.time() - t0:.1f}s", flush=True)