Claude
d8e849f615
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Tw9fjVsBg3ixjnT1Fgdy12
464 lines
20 KiB
Python
464 lines
20 KiB
Python
"""What the loaders actually do with what they are given.
|
|
|
|
Real weights cannot be loaded here (this machine's model files are zero-byte
|
|
placeholders), so the file-reading boundary is stubbed and everything on OUR side
|
|
of it runs for real: slot ordering, empty slots, no-op strengths, and where the
|
|
attention override is installed.
|
|
"""
|
|
|
|
import pytest
|
|
|
|
|
|
@pytest.fixture(autouse=True)
|
|
def _needs_comfy(comfyui):
|
|
"""Every test here imports a node module, which imports comfy."""
|
|
|
|
|
|
def test_empty_encoder_slots_are_skipped_not_looked_up(monkeypatch):
|
|
"""An empty optional slot means "no file here". Passing "" through to
|
|
get_full_path_or_raise turns a normal one-encoder setup into a hard error."""
|
|
import comfy.sd
|
|
import folder_paths
|
|
from modules.loaders.clip import nodes
|
|
|
|
asked = []
|
|
monkeypatch.setattr(folder_paths, "get_full_path_or_raise",
|
|
lambda kind, name: asked.append(name) or f"/fake/{name}")
|
|
monkeypatch.setattr(folder_paths, "get_folder_paths", lambda kind: [])
|
|
monkeypatch.setattr(comfy.sd, "load_clip", lambda **kw: object())
|
|
|
|
nodes.FunUICLIPLoader.execute(
|
|
clip_name1="encoder.safetensors", type="ltxv",
|
|
clip_name2="", clip_name3=None, clip_name4="")
|
|
|
|
assert asked == ["encoder.safetensors"]
|
|
|
|
|
|
def test_encoder_slots_load_in_slot_order(monkeypatch):
|
|
"""load_clip cares about order: the encoder comes before its connector.
|
|
Reading **kwargs in arrival order would make that arbitrary."""
|
|
import comfy.sd
|
|
import folder_paths
|
|
from modules.loaders.clip import nodes
|
|
|
|
seen = {}
|
|
monkeypatch.setattr(folder_paths, "get_full_path_or_raise", lambda kind, name: f"/fake/{name}")
|
|
monkeypatch.setattr(folder_paths, "get_folder_paths", lambda kind: [])
|
|
monkeypatch.setattr(comfy.sd, "load_clip",
|
|
lambda **kw: seen.update(kw) or object())
|
|
|
|
# Deliberately out of order in the call, which is how kwargs can arrive.
|
|
nodes.FunUICLIPLoader.execute(
|
|
clip_name3="third.safetensors", clip_name2="second.safetensors",
|
|
clip_name1="first.safetensors", type="ltxv")
|
|
|
|
assert seen["ckpt_paths"] == ["/fake/first.safetensors",
|
|
"/fake/second.safetensors",
|
|
"/fake/third.safetensors"]
|
|
|
|
|
|
def test_an_unrecognised_encoder_fails_here_not_at_encode_time(monkeypatch):
|
|
"""comfy returns None rather than raising, and a None CLIP fails much later,
|
|
a long way from the wrong file that caused it."""
|
|
import comfy.sd
|
|
import folder_paths
|
|
from modules.loaders.clip import nodes
|
|
|
|
monkeypatch.setattr(folder_paths, "get_full_path_or_raise", lambda kind, name: f"/fake/{name}")
|
|
monkeypatch.setattr(folder_paths, "get_folder_paths", lambda kind: [])
|
|
monkeypatch.setattr(comfy.sd, "load_clip", lambda **kw: None)
|
|
|
|
with pytest.raises(RuntimeError, match="Could not load a text encoder"):
|
|
nodes.FunUICLIPLoader.execute(clip_name1="wrong.safetensors", type="ltxv")
|
|
|
|
|
|
def test_no_encoder_at_all_is_refused(monkeypatch):
|
|
from modules.loaders.clip import nodes
|
|
with pytest.raises(RuntimeError, match="at least one"):
|
|
nodes.FunUICLIPLoader.execute(clip_name1="", type="ltxv")
|
|
|
|
|
|
def test_a_lora_at_zero_strength_passes_the_originals_through(monkeypatch):
|
|
"""Not clones. A clone here silently discards patches an earlier loader in
|
|
the chain applied to the same objects."""
|
|
import comfy.sd
|
|
from modules.loaders.lora import nodes
|
|
|
|
def _explode(*a, **k):
|
|
raise AssertionError("a zero-strength LoRA still read the file")
|
|
|
|
monkeypatch.setattr(comfy.sd, "load_lora_for_models", _explode)
|
|
|
|
model, clip = object(), object()
|
|
out = nodes.FunUILoraLoader.execute(
|
|
model=model, lora_name="x.safetensors", strength_model=0.0,
|
|
clip=clip, strength_clip=0.0)
|
|
|
|
assert out.result[0] is model and out.result[1] is clip
|
|
|
|
|
|
def test_a_model_only_lora_still_applies_at_zero_clip_strength(monkeypatch):
|
|
"""clip is unwired, so strength_clip is meaningless -- but strength_model is
|
|
not, and skipping the whole thing would silently drop the LoRA."""
|
|
import comfy.utils
|
|
import folder_paths
|
|
from modules.loaders.lora import nodes
|
|
|
|
added = {}
|
|
|
|
class M:
|
|
def clone(self):
|
|
return self
|
|
|
|
def add_patches(self, patches, strength):
|
|
added.update(n=len(patches), s=strength)
|
|
|
|
monkeypatch.setattr(folder_paths, "get_full_path_or_raise", lambda kind, name: f"/fake/{name}")
|
|
monkeypatch.setattr(comfy.utils, "load_torch_file", lambda p, **kw: {"fake": 1})
|
|
monkeypatch.setattr(nodes, "match", lambda m, c, l: ({"w": object()}, "as-is", 0, 0))
|
|
out = nodes.FunUILoraLoader.execute(model=M(), lora_name="x.safetensors", strength_model=0.8, clip=None)
|
|
assert added == {"n": 1, "s": 0.8}
|
|
assert "1 weights" in out.result[2]
|
|
|
|
|
|
def _lora_world(monkeypatch, weight_shape=(4, 3)):
|
|
import torch
|
|
import comfy.lora
|
|
|
|
class Inner:
|
|
def state_dict(self):
|
|
return {"diffusion_model.blocks.0.attn.weight": torch.zeros(weight_shape)}
|
|
|
|
class M:
|
|
model = Inner()
|
|
monkeypatch.setattr(comfy.lora, "model_lora_keys_unet", lambda m, km: {**km, "diffusion_model.blocks.0.attn": "diffusion_model.blocks.0.attn.weight"})
|
|
return M()
|
|
|
|
|
|
def test_a_lora_under_a_training_wrapper_is_unwrapped_and_lands(monkeypatch):
|
|
# v4: an H3 adapter keyed base_model.model.dit.* matched 0 of 532 keys and did nothing, silently.
|
|
import torch
|
|
from modules.loaders.lora.keys import match
|
|
model = _lora_world(monkeypatch)
|
|
lora = {"base_model.model.dit.blocks.0.attn.lora_up.weight": torch.zeros(4, 2),
|
|
"base_model.model.dit.blocks.0.attn.lora_down.weight": torch.zeros(2, 3)}
|
|
patches, how, dropped, _unplaced = match(model, None, lora)
|
|
assert list(patches) == ["diffusion_model.blocks.0.attn.weight"] and dropped == 0 and "dit." in how
|
|
|
|
|
|
def test_a_lora_pair_of_the_wrong_shape_is_dropped_not_merged_scrambled(monkeypatch):
|
|
import torch
|
|
from modules.loaders.lora.keys import match
|
|
model = _lora_world(monkeypatch, weight_shape=(6, 2)) # same element count (12), other shape
|
|
lora = {"diffusion_model.blocks.0.attn.lora_up.weight": torch.zeros(4, 2),
|
|
"diffusion_model.blocks.0.attn.lora_down.weight": torch.zeros(2, 3)}
|
|
patches, _how, dropped, _unplaced = match(model, None, lora)
|
|
assert patches == {} and dropped == 1
|
|
|
|
|
|
def test_a_lora_that_matches_nothing_is_not_applied_and_says_so(monkeypatch):
|
|
import comfy.utils
|
|
import folder_paths
|
|
from modules.loaders.lora import nodes
|
|
monkeypatch.setattr(folder_paths, "get_full_path_or_raise", lambda kind, name: f"/fake/{name}")
|
|
monkeypatch.setattr(comfy.utils, "load_torch_file", lambda p, **kw: {})
|
|
monkeypatch.setattr(nodes, "match", lambda m, c, l: ({}, "as-is", 0, 0))
|
|
model = object()
|
|
out = nodes.FunUILoraLoader.execute(model=model, lora_name="x.safetensors", strength_model=1.0, clip=None)
|
|
assert out.result[0] is model and "matched nothing" in out.result[2]
|
|
|
|
|
|
def test_the_attention_override_calls_the_unwrapped_backend(monkeypatch):
|
|
"""ComfyUI wraps each backend with wrap_attn and hands the override the
|
|
ORIGINAL function. Calling the wrapped one re-enters that machinery."""
|
|
from comfy.ldm.modules import attention as attn
|
|
from modules.loaders import common
|
|
|
|
calls = []
|
|
|
|
def wrapped(*args, **kwargs):
|
|
calls.append("wrapped")
|
|
|
|
def unwrapped(*args, **kwargs):
|
|
calls.append("unwrapped")
|
|
return "result"
|
|
|
|
wrapped.__wrapped__ = unwrapped
|
|
monkeypatch.setattr(attn, "get_attention_function", lambda name, default: wrapped)
|
|
|
|
override = common.attention_override("anything")
|
|
assert override(wrapped, "q", "k", "v") == "result"
|
|
assert calls == ["unwrapped"], "the override re-entered wrap_attn"
|
|
|
|
|
|
def test_default_attention_installs_nothing(monkeypatch):
|
|
"""'default' must leave ComfyUI's own launch-flag choice alone."""
|
|
from modules.loaders import common
|
|
assert common.attention_override("default") is None
|
|
assert common.attention_override("") is None
|
|
assert common.attention_override(None) is None
|
|
|
|
|
|
def test_an_unavailable_backend_does_not_silently_become_a_broken_override(monkeypatch):
|
|
from comfy.ldm.modules import attention as attn
|
|
from modules.loaders import common
|
|
monkeypatch.setattr(attn, "get_attention_function", lambda name, default: None)
|
|
assert common.attention_override("sage") is None
|
|
|
|
|
|
def test_an_unknown_encoder_family_is_refused_not_silently_downgraded(monkeypatch):
|
|
"""Falling back to STABLE_DIFFUSION loads against the wrong family and
|
|
reports success. A family mismatch here reads as an unrelated fault."""
|
|
from modules.loaders.clip import nodes
|
|
with pytest.raises(RuntimeError, match="no encoder family"):
|
|
nodes.FunUICLIPLoader.execute(clip_name1="e.safetensors", type="not_a_family")
|
|
|
|
|
|
def test_changing_the_process_wide_fp16_flag_is_never_silent(monkeypatch):
|
|
"""torch has ONE flag for the interpreter, so this cannot mean "for this
|
|
model". A second loader setting it differently changes the first model's
|
|
maths, and ComfyUI's loader caching means the last value outlives its run."""
|
|
import torch
|
|
from core import log
|
|
from modules.loaders import common
|
|
|
|
class FakeMatmul:
|
|
allow_fp16_accumulation = False
|
|
|
|
fake = FakeMatmul()
|
|
monkeypatch.setattr(torch.backends, "cuda", type("C", (), {"matmul": fake}))
|
|
|
|
log._reset()
|
|
assert common.set_fp16_accumulation(True) is True
|
|
assert fake.allow_fp16_accumulation is True
|
|
assert any("EVERY model in this process" in r["message"] for r in log.history())
|
|
|
|
# No transition, nothing said.
|
|
log._reset()
|
|
common.set_fp16_accumulation(True)
|
|
assert log.history() == []
|
|
|
|
# And turning it back off is a transition too: it is another model's setting
|
|
# being undone.
|
|
log._reset()
|
|
common.set_fp16_accumulation(False)
|
|
assert log.history()
|
|
|
|
|
|
def test_a_torch_build_without_the_flag_reports_that_rather_than_pretending():
|
|
import torch
|
|
from modules.loaders import common
|
|
if getattr(getattr(torch.backends, "cuda", None), "matmul", None) is None:
|
|
assert common.set_fp16_accumulation(True) is None
|
|
|
|
|
|
# --- GGUF routing: does the WIRING actually reach gguf_support.py, not just ---
|
|
# --- whether gguf_support.py itself works in isolation -------------------------
|
|
|
|
def test_a_gguf_model_name_is_routed_through_gguf_support(monkeypatch):
|
|
"""A .gguf model file must never reach load_torch_file -- that parser
|
|
reads a safetensors header, and a GGUF container is not one."""
|
|
import comfy.sd
|
|
from modules.loaders import gguf_support
|
|
from modules.loaders.diffusion_model import nodes
|
|
|
|
monkeypatch.setattr(gguf_support, "gguf_path", lambda folder, name: f"/fake/{name}")
|
|
monkeypatch.setattr(gguf_support, "load_state_dict",
|
|
lambda path: ({"w": 1}, {"custom_operations": "ops"}, "gguf: quantized"))
|
|
monkeypatch.setattr(comfy.sd, "load_diffusion_model_state_dict",
|
|
lambda sd, model_options, metadata: object() if sd == {"w": 1}
|
|
and model_options.get("custom_operations") == "ops" else None)
|
|
|
|
out = nodes.FunUIDiffusionModelLoader.execute(
|
|
model_name="model-Q4_K_M.gguf", weight_dtype="default",
|
|
compute_dtype="default", attention="default")
|
|
|
|
assert out.result[0] is not None
|
|
assert "gguf: quantized" in out.result[1]
|
|
|
|
|
|
def test_a_safetensors_named_file_that_is_actually_gguf_is_still_routed(monkeypatch):
|
|
"""The CONTENT decides, not the extension -- a .gguf renamed to
|
|
.safetensors must not reach the safetensors parser and fail with a
|
|
decode error that names nothing useful."""
|
|
import comfy.sd
|
|
import folder_paths
|
|
from modules.loaders import gguf_support
|
|
from modules.loaders.diffusion_model import nodes
|
|
|
|
monkeypatch.setattr(folder_paths, "get_full_path_or_raise", lambda kind, name: "/fake/path")
|
|
monkeypatch.setattr(gguf_support, "has_gguf_magic", lambda path: True)
|
|
monkeypatch.setattr(gguf_support, "load_state_dict",
|
|
lambda path: ({"w": 1}, {}, "gguf: dequantized at load"))
|
|
monkeypatch.setattr(comfy.sd, "load_diffusion_model_state_dict",
|
|
lambda sd, model_options, metadata: object())
|
|
|
|
out = nodes.FunUIDiffusionModelLoader.execute(
|
|
model_name="model.safetensors", weight_dtype="default",
|
|
compute_dtype="default", attention="default")
|
|
|
|
assert "named .safetensors but is a GGUF container" in out.result[1]
|
|
|
|
|
|
def test_an_ordinary_safetensors_model_never_touches_gguf_support(monkeypatch):
|
|
"""The common case must not pay for the uncommon one: a real
|
|
.safetensors file goes through load_torch_file, not gguf_support."""
|
|
import comfy.sd
|
|
import comfy.utils
|
|
import folder_paths
|
|
from modules.loaders import gguf_support
|
|
from modules.loaders.diffusion_model import nodes
|
|
|
|
monkeypatch.setattr(folder_paths, "get_full_path_or_raise", lambda kind, name: "/fake/path")
|
|
monkeypatch.setattr(gguf_support, "has_gguf_magic", lambda path: False)
|
|
monkeypatch.setattr(gguf_support, "load_state_dict",
|
|
lambda path: (_ for _ in ()).throw(AssertionError("gguf path taken")))
|
|
monkeypatch.setattr(comfy.utils, "load_torch_file",
|
|
lambda path, return_metadata: ({"w": 1}, {}))
|
|
monkeypatch.setattr(comfy.sd, "load_diffusion_model_state_dict",
|
|
lambda sd, model_options, metadata: object())
|
|
|
|
nodes.FunUIDiffusionModelLoader.execute(
|
|
model_name="model.safetensors", weight_dtype="default",
|
|
compute_dtype="default", attention="default")
|
|
|
|
|
|
def test_a_gguf_text_encoder_is_routed_through_gguf_support(monkeypatch):
|
|
"""A .gguf encoder cannot go through load_clip -- it reads files itself
|
|
and does not understand the container."""
|
|
import comfy.sd
|
|
from modules.loaders import gguf_support
|
|
from modules.loaders.clip import nodes
|
|
|
|
monkeypatch.setattr(gguf_support, "gguf_path", lambda folder, name: f"/fake/{name}")
|
|
monkeypatch.setattr(gguf_support, "load_clip_state_dict",
|
|
lambda path: ({"w": 1}, {}, "gguf: quantized"))
|
|
monkeypatch.setattr(comfy.sd, "load_text_encoder_state_dicts",
|
|
lambda state_dicts, **kw: object() if state_dicts == [{"w": 1}] else None)
|
|
|
|
out = nodes.FunUICLIPLoader.execute(clip_name1="encoder-Q5_K.gguf", type="ltxv")
|
|
|
|
assert out.result[0] is not None
|
|
assert "gguf: quantized" in out.result[1]
|
|
|
|
|
|
def test_a_gguf_encoder_alongside_a_plain_connector_loads_both(monkeypatch):
|
|
"""LTX-2.3's normal shape: a GGUF encoder plus a .safetensors connector
|
|
in the same list. Each slot must be read the way ITS file needs, not
|
|
however the first slot in the list happened to be."""
|
|
import comfy.sd
|
|
import comfy.utils
|
|
import folder_paths
|
|
from modules.loaders import gguf_support
|
|
from modules.loaders.clip import nodes
|
|
|
|
monkeypatch.setattr(gguf_support, "gguf_path", lambda folder, name: f"/fake/{name}")
|
|
monkeypatch.setattr(gguf_support, "load_clip_state_dict",
|
|
lambda path: ({"gguf": 1}, {}, "gguf: quantized"))
|
|
monkeypatch.setattr(folder_paths, "get_full_path_or_raise", lambda kind, name: f"/fake/{name}")
|
|
monkeypatch.setattr(comfy.utils, "load_torch_file", lambda path: {"plain": 1})
|
|
seen = {}
|
|
monkeypatch.setattr(comfy.sd, "load_text_encoder_state_dicts",
|
|
lambda state_dicts, **kw: seen.update(sd=state_dicts) or object())
|
|
|
|
nodes.FunUICLIPLoader.execute(
|
|
clip_name1="encoder-Q5_K.gguf", clip_name2="connector.safetensors", type="ltxv")
|
|
|
|
assert seen["sd"] == [{"gguf": 1}, {"plain": 1}]
|
|
|
|
|
|
def test_a_gguf_encoder_that_vanished_since_being_listed_is_refused_by_name(monkeypatch):
|
|
"""gguf_path() returns None once a listed file is gone. Without a check,
|
|
that None reaches load_clip_state_dict and fails deep inside gguf/pack
|
|
machinery instead of naming the missing file, the way the diffusion model
|
|
loader already refuses the identical condition."""
|
|
from modules.loaders import gguf_support
|
|
from modules.loaders.clip import nodes
|
|
|
|
monkeypatch.setattr(gguf_support, "gguf_path", lambda folder, name: None)
|
|
|
|
with pytest.raises(RuntimeError, match="encoder-Q5_K.gguf.*no longer where it was listed"):
|
|
nodes.FunUICLIPLoader.execute(clip_name1="encoder-Q5_K.gguf", type="ltxv")
|
|
|
|
|
|
def test_no_lora_picked_passes_the_originals_through_without_reading_a_file():
|
|
from modules.loaders.lora import nodes
|
|
model, clip = object(), object()
|
|
out = nodes.FunUILoraLoader.execute(model=model, lora_name="None", strength_model=1.0, clip=clip)
|
|
assert out.result[0] is model and out.result[1] is clip
|
|
|
|
|
|
def test_a_forced_dtype_the_model_does_not_run_in_is_said():
|
|
# v4: fp16 broke MiniMax H3, which ComfyUI lists as bf16/fp32 only.
|
|
import torch
|
|
from types import SimpleNamespace as NS
|
|
from modules.loaders.common import unsupported_dtypes
|
|
h3 = NS(model=NS(model_config=NS(supported_inference_dtypes=[torch.bfloat16, torch.float32])))
|
|
assert len(unsupported_dtypes(h3, "fp16", "default")) == 1 and "fp16" in unsupported_dtypes(h3, "fp16", "default")[0]
|
|
assert unsupported_dtypes(h3, "default", "bf16") == []
|
|
assert unsupported_dtypes(h3, "fp8_e4m3fn", "fp16")[0].startswith("compute dtype fp16")
|
|
|
|
|
|
def test_fp32_compute_over_bf16_weights_is_said():
|
|
from modules.loaders.common import fp32_over_bf16
|
|
assert fp32_over_bf16("default", "fp32", "bf16") and fp32_over_bf16("bf16", "fp32")
|
|
assert fp32_over_bf16("fp32", "fp32", "bf16") is None and fp32_over_bf16("default", "bf16", "bf16") is None
|
|
assert fp32_over_bf16("default", "fp32", "int8+bf16") is None
|
|
|
|
|
|
def test_the_vae_loader_hands_the_file_header_to_the_vae(comfyui, tmp_path, monkeypatch):
|
|
"""An audio VAE refuses to build without the config in its file's header ("Metadata is required for audio VAE")."""
|
|
import comfy.sd
|
|
import folder_paths
|
|
import torch
|
|
from safetensors.torch import save_file
|
|
from modules.loaders.vae.nodes import FunUIVAELoader
|
|
path = tmp_path / "audio_vae.safetensors"
|
|
save_file({"audio_vae.x": torch.zeros(1)}, str(path), metadata={"config": "{}"})
|
|
seen = {}
|
|
|
|
class Vae:
|
|
first_stage_model = object() # the loader refuses a VAE that built no model
|
|
|
|
def __init__(self, sd=None, dtype=None, metadata=None, **kw):
|
|
seen.update(keys=list(sd), metadata=metadata)
|
|
|
|
def throw_exception_if_invalid(self):
|
|
pass
|
|
monkeypatch.setattr(folder_paths, "get_full_path_or_raise", lambda kind, name: str(path))
|
|
monkeypatch.setattr(comfy.sd, "VAE", Vae)
|
|
FunUIVAELoader.execute("audio_vae.safetensors", "bfloat16")
|
|
assert seen["metadata"] == {"config": "{}"} and seen["keys"] == ["autoencoder.x"]
|
|
|
|
|
|
def test_several_loras_in_one_node_apply_in_order(monkeypatch):
|
|
import comfy.utils
|
|
import folder_paths
|
|
from modules.loaders.lora import nodes
|
|
|
|
seen = []
|
|
|
|
class M:
|
|
def clone(self):
|
|
return self
|
|
|
|
def add_patches(self, patches, strength):
|
|
seen.append((patches["tag"], strength))
|
|
|
|
monkeypatch.setattr(folder_paths, "get_full_path_or_raise", lambda kind, name: name)
|
|
monkeypatch.setattr(comfy.utils, "load_torch_file", lambda p, **kw: {"name": p})
|
|
monkeypatch.setattr(nodes, "match", lambda m, c, l: ({"tag": l["name"]}, "as-is", 0, 0))
|
|
out = nodes.FunUILoraLoader.execute(
|
|
model=M(), lora_name="a", strength_model=0.5,
|
|
lora_name_2="None", lora_name_3="c", strength_model_3=1.5,
|
|
lora_name_8="h", strength_model_8=-1.0)
|
|
assert seen == [("a", 0.5), ("c", 1.5), ("h", -1.0)]
|
|
assert out.result[2].count("FunUI LoRA Loader |") == 3
|
|
|
|
|
|
def test_a_skipped_slot_between_two_loras_does_not_break_the_chain(monkeypatch):
|
|
from modules.loaders.lora import nodes
|
|
model = object()
|
|
out = nodes.FunUILoraLoader.execute(model=model, lora_name="None", lora_name_2="None")
|
|
assert out.result[0] is model and out.result[2] == "no LoRA picked"
|