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Scene render adapter (phase-2 swap for video_job.render_now).
Thin, asyncio-friendly wrapper over the existing ComfyUI /
Animate pipeline at ``app.comfy.run_workflow`` + the shared
``app.asset_registry``. No new pipeline logic — just the glue
that turns a (scene_prompt, duration_sec) pair into a registered
asset id.
Every failure path returns ``None`` so the caller (video_job.
render_now) can gracefully fall back to the stub path and keep
the player's polling loop alive.
"""
from __future__ import annotations
import asyncio
import logging
import os
from typing import Any, Dict, List, Optional
from .edit_recipes import EditRecipe, recipe_to_variables
from .persona_assets import PersonaAssets, resolve_persona_assets
from .playback_config import PlaybackConfig, load_playback_config
log = logging.getLogger(__name__)
_DEFAULT_NEGATIVE = (
"blurry, low quality, watermark, extra fingers, deformed, mutated, "
"distorted face, low-res, jpeg artifacts"
)
_IMAGE_SUFFIXES = (".png", ".jpg", ".jpeg", ".webp", ".gif", ".bmp", ".tiff")
_VIDEO_SUFFIXES = (".mp4", ".mov", ".mkv", ".webm", ".avi")
async def render_scene_async(
*,
scene_prompt: str,
duration_sec: int,
session_id: str,
persona_hint: str = "",
media_type: str = "video",
config: Optional[PlaybackConfig] = None,
edit_recipe: Optional[Any] = None,
persona_project_id: str = "",
user_id: str = "",
) -> Optional[str]:
"""Submit a workflow, extract the first usable media URL, and
register it as a durable asset. Returns the asset id or
``None`` on any failure / when the render flag is off.
``media_type='image'`` swaps to the still-image workflow
(``PlaybackConfig.image_workflow``) — a fast GPU-friendly path
for operators without the VRAM budget for full video clips. The
player already picks ``<img>`` vs ``<video>`` by file extension,
so the change is transparent to the frontend.
"""
cfg = config or load_playback_config()
if not cfg.render_enabled:
return None
if not scene_prompt.strip():
return None
# Persona Live Play edit path — when the composer returned an
# edit recipe AND we can resolve the persona's canonical
# portrait on disk, route through the matching edit workflow
# (avatar_body_pose / avatar_inpaint_outfit / …) with the
# portrait as the source image. This swaps txt2img for img2img
# on a fixed anchor, which keeps identity locked across turns.
# Any failure (no persona, no portrait committed, unknown
# workflow file) falls back to the existing txt2img pipeline
# so standard projects and degraded personas still render.
persona_assets: Optional[PersonaAssets] = None
if edit_recipe and persona_project_id:
persona_assets = resolve_persona_assets(persona_project_id)
if edit_recipe and not persona_assets:
recipe_source = _recipe_source_image(edit_recipe)
if recipe_source:
persona_assets = PersonaAssets(
persona_project_id=persona_project_id or "runtime",
portrait_url=recipe_source,
portrait_path=recipe_source,
)
if edit_recipe and persona_assets:
workflow = _recipe_workflow_id(edit_recipe)
variables = _build_edit_variables(
scene_prompt=scene_prompt,
duration_sec=duration_sec,
persona_hint=persona_hint,
persona_assets=persona_assets,
recipe=edit_recipe,
)
elif edit_recipe and not persona_assets:
# Persona Live action path but the portrait couldn't be resolved.
# Falling through to txt2img here produces the "blush → empty
# room, character gone" bug: the scene prompt renders a generic
# backdrop without the persona anchor, and the viewport swaps to
# a frame that has no character in it. Bail out with a clear log
# line so the polling loop treats this as a failed render and the
# UI keeps showing the previous frame.
log.warning(
"playback_render_abort reason=persona_assets_missing "
"persona_project_id=%s recipe_workflow=%s",
persona_project_id or "(none)",
_recipe_workflow_id(edit_recipe),
)
return None
else:
# Pick the workflow by model ARCHITECTURE (sd15/sdxl/flux/svd/
# ltx/wan/...) — same dispatch Imagine + Animate already use,
# so we inherit their battle-tested workflow JSON filenames
# instead of the hard-coded ``animate`` / ``avatar_txt2img``
# that never matched files on disk. See _resolve_workflow().
workflow = _resolve_workflow(cfg, media_type)
variables = _build_variables(
scene_prompt, duration_sec, persona_hint,
media_type=media_type,
)
# Standard Interactive video safeguard. The i2v workflows
# (img2vid-ltx / img2vid / img2vid-wan / …) all require an
# ``{{image_path}}`` source frame — which we don't have for
# Standard projects (no persona portrait). Without this guard
# the workflow submits with image_path="" and ComfyUI's
# LoadImage node rejects the prompt with
# "Custom validation failed for node: image - Invalid image
# file: ". The right long-term fix is two-pass txt2img →
# img2vid; until that lands, fall back to txt2img and let
# the player surface a still image. The player handles
# image-as-video gracefully (renders <img>, plays for
# ``duration_sec`` then fires the choice modal).
if (
(media_type or "").lower() == "video"
and "img2vid" in workflow
and not str(variables.get("image_path") or "").strip()
):
log.info(
"playback_render_fallback reason=no_source_image "
"from=%s to=image", workflow,
extra={"session_id": session_id},
)
workflow = _resolve_workflow(cfg, "image")
variables = _build_variables(
scene_prompt, duration_sec, persona_hint,
media_type="image",
)
media_type = "image"
log.info(
"playback_render_submit workflow=%s media_type=%s",
workflow, media_type,
extra={"session_id": session_id},
)
try:
result = await asyncio.wait_for(
_run_workflow_off_thread(workflow, variables),
timeout=cfg.render_timeout_s,
)
except asyncio.TimeoutError:
log.warning(
"playback_render_timeout after %.1fs (workflow=%s)",
cfg.render_timeout_s, workflow,
extra={"session_id": session_id},
)
return None
except Exception as exc: # noqa: BLE001 — any pipeline failure → fallback
log.warning(
"playback_render_error (workflow=%s): %s",
workflow, str(exc)[:400],
extra={"session_id": session_id},
)
return None
media_url, media_kind = _select_best_media(result, prefer=media_type)
if not media_url:
keys = list(result.keys()) if isinstance(result, dict) else []
log.warning(
"playback_render_no_output — run_workflow returned keys %s but no videos/images",
keys,
extra={"session_id": session_id},
)
return None
try:
asset_id = _register(
storage_key=media_url, kind=media_kind,
session_id=session_id, scene_prompt=scene_prompt,
user_id=user_id,
)
except Exception as exc: # noqa: BLE001
log.warning(
"playback_asset_register_failed: %s", str(exc)[:200],
extra={"session_id": session_id},
)
return None
return asset_id
# ── Internals ───────────────────────────────────────────────────
# Architecture → image txt2img workflow. Mirrors the mapping
# ``app.orchestrator`` (Imagine) already uses, so we submit the
# same JSON files that Imagine is known to render correctly.
_IMAGE_WORKFLOW_BY_ARCH: Dict[str, str] = {
"sd15": "txt2img-sd15-uncensored",
"sdxl": "txt2img",
"flux_schnell": "txt2img-flux-schnell",
"flux_dev": "txt2img-flux-dev",
"noobai_xl": "txt2img",
"noobai_xl_vpred": "txt2img",
"pony_xl": "txt2img-pony-xl",
}
# Substring in the video model filename → img2vid workflow.
# Same dispatch Animate uses (orchestrator lines ~995-1015).
_VIDEO_WORKFLOW_BY_MODEL: List[tuple[str, str]] = [
("ltx", "img2vid-ltx"),
("wan", "img2vid-wan"),
("mochi", "img2vid-mochi"),
("hunyuan", "img2vid-hunyuan"),
("cogvideo", "img2vid-cogvideo"),
("svd", "img2vid"),
]
def _pick_available_clip_model(
*, prefer: tuple[str, ...], fallback: str,
) -> str:
"""Pick a CLIP/T5 encoder filename that ComfyUI actually has.
Queries the cached ``/object_info`` for ``CLIPLoader.clip_name`` and
returns the first installed model whose lowercase name contains any
of the ``prefer`` substrings (in order). Falls back to the provided
default when ComfyUI is unreachable or has no matching model.
Used to resolve ``{{t5_encoder}}`` in LTX-style video workflows so
we submit the real installed filename instead of a literal
``{{t5_encoder}}`` string. Lets operators keep their model
configuration as-is even when the env/settings don't name the
encoder explicitly — we just use whatever's on disk.
"""
try:
from ...comfy import _fetch_object_info # late import
info = _fetch_object_info()
except Exception: # noqa: BLE001 — ComfyUI down / partial install
return fallback
clip_loader = info.get("CLIPLoader", {}) if isinstance(info, dict) else {}
input_spec = clip_loader.get("input", {}) if isinstance(clip_loader, dict) else {}
required = input_spec.get("required", {}) if isinstance(input_spec, dict) else {}
clip_input = required.get("clip_name", [])
names: List[str] = []
if isinstance(clip_input, list) and clip_input and isinstance(clip_input[0], list):
names = [str(n) for n in clip_input[0] if isinstance(n, str)]
if not names:
return fallback
for needle in prefer:
for n in names:
if needle.lower() in n.lower():
return n
# No match by preference — return the first installed encoder rather
# than a hardcoded name that may not exist. "Use what it has."
return names[0]
def _resolve_workflow(cfg: PlaybackConfig, media_type: str) -> str:
"""Pick the ComfyUI workflow filename from the live-resolved
model name the same way Imagine + Animate do.
Image mode: model filename → architecture (via
``model_config.get_architecture``) → workflow from the
table above.
Video mode: substring match on the video model name (``svd``
→ ``img2vid``, ``ltx`` → ``img2vid-ltx``, …).
When no match is found we fall back to the ``PlaybackConfig``
value so operators can still override via env — but the log
line above tells us exactly what ran either way.
"""
from ..media_router import resolve_current_providers # late import
from ...model_config import detect_architecture_from_filename
providers = resolve_current_providers()
kind = (media_type or "").strip().lower()
if kind == "image":
arch = detect_architecture_from_filename(providers.image_model)
wf = _IMAGE_WORKFLOW_BY_ARCH.get(arch)
if wf:
# Pony override matches Imagine's special-case (an SDXL
# finetune with its own workflow).
if "pony" in (providers.image_model or "").lower():
return "txt2img-pony-xl"
return wf
return cfg.image_workflow # env override / "avatar_txt2img" default
# Video path (default).
model_lc = (providers.video_model or "").lower()
for needle, workflow in _VIDEO_WORKFLOW_BY_MODEL:
if needle in model_lc:
return workflow
return cfg.render_workflow # env override / "animate" default
def _build_variables(
scene_prompt: str, duration_sec: int, persona_hint: str,
*, media_type: str = "video",
) -> Dict[str, Any]:
"""Bundle a generic variable set accepted by typical Animate /
ComfyUI video templates. Unknown keys are harmless — workflow
engines ignore variables they don't reference.
PIPE-1: includes the live-resolved image + video model names
under several aliases (``image_model``, ``video_model``,
``model``, ``checkpoint``, ``ckpt_name``) so a workflow author
can reference whichever placeholder style matches their JSON.
``model`` in particular is set to the ACTIVE model for the
requested media_type, which matches the most common template
convention in public ComfyUI workflow snippets.
"""
import random as _random
from ..media_router import resolve_current_providers # late import
from ...model_config import detect_architecture_from_filename, get_model_settings
positive = ", ".join(p for p in (persona_hint.strip(), scene_prompt.strip()) if p)
safe_duration = max(2, min(int(duration_sec or 5), 15))
fps = 8
providers = resolve_current_providers()
kind = (media_type or "").lower()
active_model = (
providers.image_model if kind == "image" else providers.video_model
)
# Resolve safe width/height/steps/cfg the same way Imagine
# does, so the workflow template substitution never produces
# the "two heads" resolution mismatch SD 1.5 is famous for.
# Defaults match Imagine's "med" preset at 1:1 when the model
# is unknown.
width, height, steps, cfg_scale = 512, 512, 25, 7.0
if kind == "image":
try:
settings = get_model_settings(
providers.image_model, "1:1", "med",
)
width = int(settings["width"])
height = int(settings["height"])
steps = int(settings["steps"])
cfg_scale = float(settings["cfg"])
except Exception: # noqa: BLE001 — unknown model → defaults
pass
# Workflow-specific template variables that older _build_variables
# callers never populated. When the workflow template references
# e.g. ``{{t5_encoder}}`` and we don't provide the variable, the
# submitted prompt contains the literal string ``{{t5_encoder}}``
# and ComfyUI rejects it with
# "Value not in list: clip_name: '{{t5_encoder}}' not in [...]"
# — even though the encoder file IS installed. The fix is to pick
# a sensible default from what ComfyUI actually reports as
# available (via /object_info), falling back to the standard
# filename when the query fails.
t5_encoder = _pick_available_clip_model(
prefer=("t5xxl_fp16", "t5xxl_fp8_e4m3fn", "t5"),
fallback="t5xxl_fp16.safetensors",
)
image_path_default = os.getenv("DEFAULT_IMG2VID_SOURCE", "").strip()
return {
"prompt": positive,
"positive_prompt": positive,
"negative_prompt": os.getenv("DEFAULT_NEGATIVE_PROMPT", _DEFAULT_NEGATIVE),
"seconds": safe_duration,
"duration_sec": safe_duration,
"frames": safe_duration * fps,
"fps": fps,
# --- Render tuning (required by Imagine-style workflows) ---
"width": width,
"height": height,
"steps": steps,
"cfg": cfg_scale,
"seed": _random.randint(1, 2_147_483_646),
"aspect_ratio": "1:1",
"style": "photorealistic",
# --- Workflow placeholder defaults (so _missing_ workflow vars
# don't submit as literal "{{...}}" and get rejected) ---
"t5_encoder": t5_encoder,
"image_path": image_path_default,
"denoise": 1.0, # img2vid defaults to full denoise on the start frame
# --- PIPE-1: model + endpoint bindings ---
"image_model": providers.image_model,
"video_model": providers.video_model,
"model": active_model,
"checkpoint": active_model,
"ckpt_name": active_model,
"comfy_base_url": providers.comfy_base_url,
}
def _build_edit_variables(
*, scene_prompt: str, duration_sec: int, persona_hint: str,
persona_assets: PersonaAssets, recipe: Any,
) -> Dict[str, Any]:
"""Variables for a Persona Live Play edit run.
Reuses the same "aliased model/checkpoint" shape the txt2img
path emits (so a workflow author can reference whichever name
fits their JSON) but overlays the edit-specific fields:
source image paths, per-recipe denoise/cfg/steps, the LoRA
stack (safety-filtered upstream), and the mask / ControlNet
hints. Every key the workflow doesn't reference is ignored,
so we can send a union of inputs without per-workflow code.
"""
import random as _random
from ..media_router import resolve_current_providers # late import
positive_bits: List[str] = []
for bit in (persona_hint.strip(), persona_assets.character_prompt,
persona_assets.outfit_prompt, scene_prompt.strip()):
if bit and bit not in positive_bits:
positive_bits.append(bit)
positive = ", ".join(positive_bits)
providers = resolve_current_providers()
safe_duration = max(2, min(int(duration_sec or 5), 15))
base: Dict[str, Any] = {
"prompt": positive,
"positive_prompt": positive,
"negative_prompt": os.getenv("DEFAULT_NEGATIVE_PROMPT", _DEFAULT_NEGATIVE),
"seconds": safe_duration,
"duration_sec": safe_duration,
"width": 1024,
"height": 1024,
"steps": _recipe_param_int(recipe, "steps", 30),
"cfg": _recipe_param_float(recipe, "cfg", 5.5),
"denoise": _recipe_param_float(recipe, "denoise", 0.45),
"seed": _random.randint(1, 2_147_483_646),
"aspect_ratio": "1:1",
"style": "photorealistic",
# Source image for img2img / inpaint — multiple aliases so
# a workflow LoadImage node can pick whichever placeholder
# its JSON uses. ``image_path`` is the canonical name used by
# the retemplated avatar/* workflows; the others are kept for
# back-compat with workflows that pre-date the rewrite.
"image_path": persona_assets.portrait_path,
"source_image": persona_assets.portrait_path,
"input_image": persona_assets.portrait_path,
"image": persona_assets.portrait_path,
"reference_image": persona_assets.portrait_path,
"persona_portrait_url": persona_assets.portrait_url,
# Model bindings (same shape txt2img path uses).
"image_model": providers.image_model,
"video_model": providers.video_model,
"model": providers.image_model,
"checkpoint": providers.image_model,
"ckpt_name": providers.image_model,
"comfy_base_url": providers.comfy_base_url,
}
base.update(_recipe_to_variables_any(recipe))
return base
def _recipe_workflow_id(recipe: Any) -> str:
if isinstance(recipe, dict):
return str(recipe.get("workflow_id") or "edit").replace(".json", "")
return str(getattr(recipe, "workflow_id", "") or "edit").replace(".json", "")
def _recipe_source_image(recipe: Any) -> str:
if not isinstance(recipe, dict):
return ""
inputs = recipe.get("inputs") if isinstance(recipe.get("inputs"), dict) else {}
candidates = [
str(inputs.get("source_image") or ""),
str(inputs.get("image_ref") or ""),
str(inputs.get("input_image") or ""),
str(inputs.get("image") or ""),
]
for raw in candidates:
path = raw.strip()
if not path:
continue
if path.startswith("/files/"):
upload_dir = (os.getenv("UPLOAD_DIR") or "").strip()
if not upload_dir:
data_dir = (os.getenv("DATA_DIR") or "").strip()
if data_dir:
upload_dir = os.path.join(data_dir, "uploads")
# Final fallback: the backend's canonical config value. Root
# cause of the "blush → empty room, character vanished" bug —
# when UPLOAD_DIR / DATA_DIR aren't exported as env vars in
# the serving process, this function used to return empty,
# which dropped persona_assets to None and forced the renderer
# into generic txt2img with only the scene prompt.
if not upload_dir:
try:
from ... import config as _app_config # type: ignore
upload_dir = str(getattr(_app_config, "UPLOAD_DIR", "") or "").strip()
except Exception:
upload_dir = ""
if upload_dir:
local_path = os.path.join(upload_dir, path[len("/files/"):])
if os.path.isfile(local_path):
return local_path
if os.path.isfile(path):
return path
return ""
def _recipe_param_int(recipe: Any, key: str, default: int) -> int:
if isinstance(recipe, dict):
params = recipe.get("params") if isinstance(recipe.get("params"), dict) else {}
try:
return int(params.get(key, default))
except (TypeError, ValueError):
return default
try:
return int(getattr(recipe, key))
except Exception:
return default
def _recipe_param_float(recipe: Any, key: str, default: float) -> float:
if isinstance(recipe, dict):
params = recipe.get("params") if isinstance(recipe.get("params"), dict) else {}
try:
return float(params.get(key, default))
except (TypeError, ValueError):
return default
try:
return float(getattr(recipe, key))
except Exception:
return default
def _recipe_to_variables_any(recipe: Any) -> Dict[str, Any]:
if not isinstance(recipe, dict):
return recipe_to_variables(recipe)
params = recipe.get("params") if isinstance(recipe.get("params"), dict) else {}
locks = recipe.get("locks") if isinstance(recipe.get("locks"), list) else []
mask_kind = ""
if "face" in locks:
mask_kind = "face"
elif "background" in locks:
mask_kind = "outfit"
elif "subject" in locks:
mask_kind = "bg"
return {
"edit_mode": str(params.get("mode") or "img2img"),
"edit_steps": _recipe_param_int(recipe, "steps", 30),
"edit_cfg": _recipe_param_float(recipe, "cfg", 5.5),
"edit_denoise": _recipe_param_float(recipe, "denoise", 0.45),
"edit_controlnet": str(recipe.get("controlnet") or ""),
"edit_mask_kind": mask_kind,
"edit_lora_stack": list(recipe.get("loras") or []),
}
async def _run_workflow_off_thread(
workflow: str, variables: Dict[str, Any],
) -> Dict[str, Any]:
"""Run the sync, blocking run_workflow() on a worker thread
so the asyncio event loop stays responsive. A late import
keeps the phase-1 stub path free of the heavy comfy import.
"""
loop = asyncio.get_running_loop()
def _go() -> Dict[str, Any]:
from ...comfy import run_workflow # late import
return run_workflow(workflow, variables) or {}
result = await loop.run_in_executor(None, _go)
return result if isinstance(result, dict) else {}
def _select_best_media(
result: Dict[str, Any], *, prefer: str = "video",
) -> tuple[str, str]:
"""Pick the best output URL from a ComfyUI result.
``prefer='video'`` (default) grabs a clip first and falls back to
an image — the legacy order for full video workflows that only
occasionally produce stills. ``prefer='image'`` flips the
priority so operators using the image-only workflow (for
GPU-constrained feasibility tests) don't accidentally get an
unexpected clip if the workflow also emits one. Returns
``(url, kind)``; both empty when nothing usable is present.
"""
videos = _first_urls(result.get("videos"))
images = _first_urls(result.get("images"))
if (prefer or "").strip().lower() == "image":
if images:
return images[0], "image"
if videos:
return videos[0], "video"
return "", ""
if videos:
return videos[0], "video"
if images:
return images[0], "image"
return "", ""
def _first_urls(candidate: Any) -> List[str]:
if not isinstance(candidate, list):
return []
out: List[str] = []
for item in candidate:
if isinstance(item, str) and item.strip():
out.append(item.strip())
return out
def _register(
*, storage_key: str, kind: str,
session_id: str, scene_prompt: str,
user_id: str = "",
) -> str:
"""Register the produced asset so operators can find it later
via the same registry that the studio + animate tabs use.
Late import so the phase-1 stub path never pulls sqlite in.
``user_id`` is the experience owner — populated by callers from
``Experience.user_id`` (wizard / phase-2 scene render / phase-3
library build) and from the auth-resolved current user (Persona
Live action endpoint, lazy entry render). The ``assets`` table
has had the column + index for it since day one; previously every
interactive render row landed with ``user_id=""``, which broke
multi-tenant audit queries.
"""
from ...asset_registry import register_asset # late import
mime = _guess_mime(storage_key, kind)
return register_asset(
storage_key=storage_key,
kind=kind,
mime=mime,
origin="interactive_playback",
feature="animate",
project_id=session_id,
user_id=user_id,
source_hint=_short_hint(scene_prompt),
)
def _guess_mime(url: str, kind: str) -> str:
lower = url.lower()
if kind == "video":
for ext in _VIDEO_SUFFIXES:
if lower.endswith(ext):
return f"video/{ext.lstrip('.')}"
return "video/mp4"
for ext in _IMAGE_SUFFIXES:
if lower.endswith(ext):
ext_name = ext.lstrip(".")
if ext_name == "jpg":
ext_name = "jpeg"
return f"image/{ext_name}"
return "image/png"
def _short_hint(text: str, *, limit: int = 120) -> str:
clean = " ".join(text.split())
if len(clean) <= limit:
return clean
return clean[: limit - 1].rstrip() + "…"
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