SolitaryThinker
9e8d515842
Two distilled few-step video models rendered incorrectly on GPU; root-caused via dense frame sampling (contact sheets) and fixed in the recipe cards. SF-Wan2.1 (self-forcing causal, wan_causal card): was oversaturated/overcooked. The distilled student is CFG-FREE (guidance 1.0, single forward/step) and denoises with the 4-step DMD schedule [1000,750,500,250] (warped by FlowShiftPolicy(5.0)), at a native causal block of 3 latent frames. Defaults were ClassicCFG@6.0 + 2 steps + block 2 -> overcooked AND under-denoised. Fixed: num_chunks=7, chunk_size=3, steps_per_chunk=4; SamplingDefaults num_steps=4, guidance_scale=1.0 (7x3=21 latent -> 81 frames). Renders a clean raccoon-in-sunflowers across all 81 frames. LTX2-Distilled 2-stage SR (ltx2 card + LTX2VAE): was temporally blocky. Root cause was an OOM-forced 57-frame reduction (only 8 latent temporal frames); the model is designed for 121 (16 latent frames). Enable VAE tiling in LTX2VAE so the 121-frame full-res decode fits the 80 GiB GPU; keep base cfg_scale=3.0 (drives brightness; cfg=1 washed out) with stg_scale=0.0 (v2's drop-text perturbation is not real skip-layer STG). Renders the on-prompt backyard shot, bright + temporally coherent. torch_backend.py: enable LTX2VAE tiling; clear pre-existing mypy no-untyped-call / yapf debt on the file (surfaced once per-file linting bypassed the duplicate-module flakiness) by annotating the helper/constructor/maker signatures. Tests: update the 3 affected CPU defaults/chunk-count tests. v2 mini: 240 passed.
78 lines
3.4 KiB
Python
78 lines
3.4 KiB
Python
"""Phase 1a: per-model sampling defaults on the card + the kwargs>SamplingParam>card>generic precedence."""
|
|
from types import SimpleNamespace
|
|
|
|
from v2.card import SamplingDefaults
|
|
from v2.recipes.ltx2 import build_ltx2_3_card, build_ltx2_base_card, build_ltx2_card
|
|
from v2.recipes.wan21 import (
|
|
build_wan21_card,
|
|
build_wan22_a14b_card,
|
|
build_wan22_ti2v_card,
|
|
build_wan_t2v_14b_card,
|
|
)
|
|
from v2.recipes.wan_causal import build_wan_causal_card
|
|
from v2.registry import resolve
|
|
from v2.video_generator import _resolve_default
|
|
|
|
|
|
def test_wan_t2v_14b_defaults_and_registry():
|
|
# Bucket-B port: reuses the Wan recipe/adapter, only defaults differ (720p, flow_shift 5.0).
|
|
sd = build_wan_t2v_14b_card().sampling_defaults
|
|
assert (sd.num_steps, sd.guidance_scale, sd.height, sd.width, sd.fps) == (50, 5.0, 720, 1280, 16)
|
|
# the HF id resolves to the 14B card via the shared registry (not the 1.3B fallback defaults)
|
|
build_card, _ = resolve("Wan-AI/Wan2.1-T2V-14B-Diffusers")
|
|
assert build_card().sampling_defaults.height == 720
|
|
|
|
|
|
def test_wan21_defaults():
|
|
sd = build_wan21_card().sampling_defaults
|
|
assert (sd.num_steps, sd.guidance_scale, sd.height, sd.width, sd.num_frames, sd.fps) == (50, 3.0, 480, 832, 81, 16)
|
|
assert "overexposed" in sd.negative_prompt
|
|
|
|
|
|
def test_wan22_ti2v_defaults():
|
|
sd = build_wan22_ti2v_card().sampling_defaults
|
|
assert (sd.num_steps, sd.guidance_scale, sd.height, sd.width, sd.num_frames, sd.fps) == (50, 5.0, 704, 1280, 121, 24)
|
|
|
|
|
|
def test_wan22_a14b_defaults():
|
|
sd = build_wan22_a14b_card().sampling_defaults
|
|
assert (sd.num_steps, sd.guidance_scale, sd.fps) == (40, 4.0, 16)
|
|
|
|
|
|
def test_wan_causal_defaults():
|
|
sd = build_wan_causal_card().sampling_defaults
|
|
assert (sd.num_steps, sd.guidance_scale, sd.height, sd.width, sd.num_frames, sd.fps) == (4, 1.0, 480, 832, 81, 16)
|
|
|
|
|
|
def test_ltx2_two_stage_defaults():
|
|
sd = build_ltx2_card().sampling_defaults
|
|
assert (sd.num_steps, sd.guidance_scale, sd.height, sd.width, sd.num_frames, sd.fps) == (8, 1.0, 1024, 1536, 121, 24)
|
|
assert sd.negative_prompt == "" # distilled: empty negative prompt
|
|
|
|
|
|
def test_ltx2_base_defaults():
|
|
sd = build_ltx2_base_card().sampling_defaults
|
|
assert (sd.num_steps, sd.guidance_scale, sd.height, sd.width) == (40, 3.0, 512, 768)
|
|
assert "blurry" in sd.negative_prompt
|
|
|
|
|
|
def test_ltx2_3_defaults():
|
|
sd = build_ltx2_3_card().sampling_defaults
|
|
assert (sd.num_steps, sd.guidance_scale, sd.height, sd.width) == (30, 3.0, 512, 768)
|
|
assert sd.guidance_per_modality == {"video": 3.0, "audio": 7.0}
|
|
|
|
|
|
def test_resolve_precedence():
|
|
sd = SamplingDefaults(num_steps=8, guidance_scale=1.0, height=1024)
|
|
# card default used when neither kwargs nor SamplingParam set it (note the num_steps alias)
|
|
assert _resolve_default("num_inference_steps", 30, {}, None, sd, "num_steps") == 8
|
|
assert _resolve_default("height", 480, {}, None, sd) == 1024
|
|
# kwargs override card
|
|
assert _resolve_default("num_inference_steps", 30, {"num_inference_steps": 4}, None, sd, "num_steps") == 4
|
|
# SamplingParam override (no kwargs) beats card
|
|
assert _resolve_default("height", 480, {}, SimpleNamespace(height=720), sd) == 720
|
|
# generic fallback when card has no value and nothing else set
|
|
assert _resolve_default("width", 832, {}, None, sd) == 832
|
|
# empty-string negative prompt is a real value, not "unset"
|
|
assert _resolve_default("negative_prompt", None, {}, None, SamplingDefaults(negative_prompt="")) == ""
|