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[Revert] drop Wan2.2 prompt-length enforcement from #2847 #2877

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Merged
gcanlin merged 2 commits into from
Apr 20, 2026
Merged

[Revert] drop Wan2.2 prompt-length enforcement from #2847 #2877

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9528def
[Revert] drop Wan2.2 prompt-length enforcement from #2847
david6666666 Apr 20, 2026
d4ac17c
[CI] re-enable Wan2.2 I2V online serving test
david6666666 Apr 20, 2026
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150 changes: 0 additions & 150 deletions tests/diffusion/models/wan2_2/test_wan22_max_sequence_length.py
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1 change: 0 additions & 1 deletion tests/e2e/accuracy/wan22_i2v/test_wan22_i2v_video_similarity.py
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Original file line number Diff line number Diff line change
Expand Up @@ -567,7 +567,6 @@ def test_wan22_i2v_diffusers_offline_generates_video(
@pytest.mark.benchmark
@pytest.mark.diffusion
@hardware_test(res={"cuda": "H100"}, num_cards=2)
@pytest.mark.skip(reason="issue: #2874")
@pytest.mark.parametrize("omni_server", SERVER_CASES, indirect=True)
def test_wan22_i2v_online_serving_generates_video(
omni_server,
Expand Down
46 changes: 2 additions & 44 deletions vllm_omni/diffusion/models/wan2_2/pipeline_wan2_2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,16 +29,12 @@
from vllm_omni.diffusion.postprocess import interpolate_video_tensor
from vllm_omni.diffusion.profiler.diffusion_pipeline_profiler import DiffusionPipelineProfilerMixin
from vllm_omni.diffusion.request import OmniDiffusionRequest
from vllm_omni.diffusion.utils.prompt_utils import (
validate_prompt_sequence_lengths,
)
from vllm_omni.inputs.data import OmniTextPrompt
from vllm_omni.platforms import current_omni_platform

logger = logging.getLogger(__name__)
DEBUG_PERF = False
WAN_SAMPLE_SOLVER_CHOICES = {"unipc", "euler"}
WAN22_MAX_SEQUENCE_LENGTH = 512


def build_wan_scheduler(sample_solver: str, flow_shift: float) -> Any:
Expand Down Expand Up @@ -293,7 +289,6 @@ def __init__(
pass

self.boundary_ratio = od_config.boundary_ratio
self.tokenizer_max_length = WAN22_MAX_SEQUENCE_LENGTH

# Determine which transformers to load based on boundary_ratio
# boundary_ratio=1.0: only load transformer_2 (low-noise stage only)
Expand Down Expand Up @@ -565,7 +560,6 @@ def forward(
negative_prompt_embeds=negative_prompt_embeds,
guidance_scale_2=guidance_high if boundary_ratio is not None else None,
boundary_ratio=boundary_ratio,
max_sequence_length=req.sampling_params.max_sequence_length or self.tokenizer_max_length,
)

if num_frames % self.vae_scale_factor_temporal != 1:
Expand Down Expand Up @@ -599,7 +593,7 @@ def forward(
negative_prompt=negative_prompt,
do_classifier_free_guidance=guidance_low > 1.0 or guidance_high > 1.0,
num_videos_per_prompt=req.sampling_params.num_outputs_per_prompt or 1,
max_sequence_length=req.sampling_params.max_sequence_length or self.tokenizer_max_length,
max_sequence_length=req.sampling_params.max_sequence_length or 512,
device=device,
dtype=dtype,
)
Expand Down Expand Up @@ -832,20 +826,6 @@ def encode_prompt(
prompt = [prompt] if isinstance(prompt, str) else prompt
prompt_clean = [self._prompt_clean(p) for p in prompt]
batch_size = len(prompt_clean)
text_inputs_untruncated = self.tokenizer(
prompt_clean,
padding=True,
truncation=False,
add_special_tokens=True,
return_attention_mask=True,
return_tensors="pt",
)
validate_prompt_sequence_lengths(
text_inputs_untruncated.attention_mask,
max_sequence_length=max_sequence_length,
supported_max_sequence_length=self.tokenizer_max_length,
error_context="for Wan2.2 text encoding",
)

text_inputs = self.tokenizer(
prompt_clean,
Expand Down Expand Up @@ -874,24 +854,8 @@ def encode_prompt(
if do_classifier_free_guidance:
negative_prompt = negative_prompt or ""
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
negative_prompt_clean = [self._prompt_clean(p) for p in negative_prompt]
neg_text_inputs_untruncated = self.tokenizer(
negative_prompt_clean,
padding=True,
truncation=False,
add_special_tokens=True,
return_attention_mask=True,
return_tensors="pt",
)
validate_prompt_sequence_lengths(
neg_text_inputs_untruncated.attention_mask,
max_sequence_length=max_sequence_length,
supported_max_sequence_length=self.tokenizer_max_length,
prompt_name="negative_prompt",
error_context="for Wan2.2 text encoding",
)
neg_text_inputs = self.tokenizer(
negative_prompt_clean,
[self._prompt_clean(p) for p in negative_prompt],
padding="max_length",
max_length=max_sequence_length,
truncation=True,
Expand Down Expand Up @@ -963,7 +927,6 @@ def check_inputs(
negative_prompt_embeds=None,
guidance_scale_2=None,
boundary_ratio=None,
max_sequence_length=None,
):
if height % 16 != 0 or width % 16 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
Expand All @@ -990,10 +953,5 @@ def check_inputs(
):
raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")

if max_sequence_length is not None and max_sequence_length > self.tokenizer_max_length:
raise ValueError(
f"`max_sequence_length` cannot be greater than {self.tokenizer_max_length} but is {max_sequence_length}"
)

if boundary_ratio is None and guidance_scale_2 is not None:
raise ValueError("`guidance_scale_2` is only supported when `boundary_ratio` is set.")
46 changes: 2 additions & 44 deletions vllm_omni/diffusion/models/wan2_2/pipeline_wan2_2_i2v.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,6 @@
from vllm_omni.diffusion.models.interface import SupportImageInput
from vllm_omni.diffusion.models.progress_bar import ProgressBarMixin, _is_rank_zero
from vllm_omni.diffusion.models.wan2_2.pipeline_wan2_2 import (
WAN22_MAX_SEQUENCE_LENGTH,
build_wan_scheduler,
create_transformer_from_config,
load_transformer_config,
Expand All @@ -37,9 +36,6 @@
from vllm_omni.diffusion.postprocess import interpolate_video_tensor
from vllm_omni.diffusion.profiler.diffusion_pipeline_profiler import DiffusionPipelineProfilerMixin
from vllm_omni.diffusion.request import OmniDiffusionRequest
from vllm_omni.diffusion.utils.prompt_utils import (
validate_prompt_sequence_lengths,
)
from vllm_omni.inputs.data import OmniTextPrompt
from vllm_omni.platforms import current_omni_platform

Expand Down Expand Up @@ -218,7 +214,6 @@ def __init__(

# Text encoder
self.tokenizer = AutoTokenizer.from_pretrained(model, subfolder="tokenizer", local_files_only=local_files_only)
self.tokenizer_max_length = WAN22_MAX_SEQUENCE_LENGTH
self.text_encoder = UMT5EncoderModel.from_pretrained(
model, subfolder="text_encoder", torch_dtype=dtype, local_files_only=local_files_only
).to(self.device)
Expand Down Expand Up @@ -474,7 +469,6 @@ def forward(
image_embeds=image_embeds,
guidance_scale_2=guidance_high if boundary_ratio is not None else None,
boundary_ratio=boundary_ratio,
max_sequence_length=req.sampling_params.max_sequence_length or self.tokenizer_max_length,
)

# Adjust num_frames to be compatible with VAE temporal scaling
Expand Down Expand Up @@ -503,7 +497,7 @@ def forward(
negative_prompt=negative_prompt,
do_classifier_free_guidance=guidance_low > 1.0 or guidance_high > 1.0,
num_videos_per_prompt=req.sampling_params.num_outputs_per_prompt or 1,
max_sequence_length=req.sampling_params.max_sequence_length or self.tokenizer_max_length,
max_sequence_length=req.sampling_params.max_sequence_length or 512,
device=device,
dtype=dtype,
)
Expand Down Expand Up @@ -708,20 +702,6 @@ def encode_prompt(
prompt = [prompt] if isinstance(prompt, str) else prompt
prompt_clean = [self._prompt_clean(p) for p in prompt]
batch_size = len(prompt_clean)
text_inputs_untruncated = self.tokenizer(
prompt_clean,
padding=True,
truncation=False,
add_special_tokens=True,
return_attention_mask=True,
return_tensors="pt",
)
validate_prompt_sequence_lengths(
text_inputs_untruncated.attention_mask,
max_sequence_length=max_sequence_length,
supported_max_sequence_length=self.tokenizer_max_length,
error_context="for Wan2.2 text encoding",
)

text_inputs = self.tokenizer(
prompt_clean,
Expand Down Expand Up @@ -750,24 +730,8 @@ def encode_prompt(
if do_classifier_free_guidance:
negative_prompt = negative_prompt or ""
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
negative_prompt_clean = [self._prompt_clean(p) for p in negative_prompt]
neg_text_inputs_untruncated = self.tokenizer(
negative_prompt_clean,
padding=True,
truncation=False,
add_special_tokens=True,
return_attention_mask=True,
return_tensors="pt",
)
validate_prompt_sequence_lengths(
neg_text_inputs_untruncated.attention_mask,
max_sequence_length=max_sequence_length,
supported_max_sequence_length=self.tokenizer_max_length,
prompt_name="negative_prompt",
error_context="for Wan2.2 text encoding",
)
neg_text_inputs = self.tokenizer(
negative_prompt_clean,
[self._prompt_clean(p) for p in negative_prompt],
padding="max_length",
max_length=max_sequence_length,
truncation=True,
Expand Down Expand Up @@ -911,7 +875,6 @@ def check_inputs(
image_embeds=None,
guidance_scale_2=None,
boundary_ratio=None,
max_sequence_length=None,
):
if image is None and image_embeds is None:
raise ValueError("Provide either `image` or `image_embeds`. Cannot leave both undefined.")
Expand All @@ -933,11 +896,6 @@ def check_inputs(
if prompt is None and prompt_embeds is None:
raise ValueError("Provide either `prompt` or `prompt_embeds`.")

if max_sequence_length is not None and max_sequence_length > self.tokenizer_max_length:
raise ValueError(
f"`max_sequence_length` cannot be greater than {self.tokenizer_max_length} but is {max_sequence_length}"
)

if boundary_ratio is None and guidance_scale_2 is not None:
raise ValueError("`guidance_scale_2` is only supported when `boundary_ratio` is set.")

Expand Down
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