Skip to content

Add SD3.5-medium quantization support in ModelOpt Diffusers example #444

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
jingyu-ml merged 2 commits into from
Oct 23, 2025
Merged

Add SD3.5-medium quantization support in ModelOpt Diffusers example #444

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
9350ecd
Add SD3.5 medium quantization support in ModelOpt Diffusers Examples
vishalpandya1990 Oct 17, 2025
dc8a02c
tox fix
vishalpandya1990 Oct 17, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
18 changes: 16 additions & 2 deletions examples/diffusers/quantization/onnx_utils/export.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -73,6 +73,13 @@
"pooled_projections": {0: "batch_size"},
"sample": {0: "batch_size", 1: "num_channels", 2: "height", 3: "width"},
},
"sd3.5-medium": {
"hidden_states": {0: "batch_size", 1: "num_channels", 2: "height", 3: "width"},
"timestep": {0: "steps"},
"encoder_hidden_states": {0: "batch_size", 1: "sequence_length"},
"pooled_projections": {0: "batch_size"},
"out_hidden_states": {0: "batch_size", 1: "num_channels", 2: "height", 3: "width"},
},
"flux-dev": {
"hidden_states": {0: "batch_size", 1: "latent_dim"},
"encoder_hidden_states": {0: "batch_size"},
Expand Down Expand Up @@ -290,6 +297,8 @@ def update_dynamic_axes(model_id, dynamic_axes):
dynamic_axes["out.0"] = dynamic_axes.pop("latent")
elif model_id == "sd3-medium":
dynamic_axes["out.0"] = dynamic_axes.pop("sample")
elif model_id == "sd3.5-medium":
dynamic_axes["out.0"] = dynamic_axes.pop("out_hidden_states")


def _create_dynamic_shapes(dynamic_shapes):
Expand All @@ -313,7 +322,7 @@ def generate_dummy_inputs_and_dynamic_axes_and_shapes(model_id, backbone):
dummy_input, dynamic_shapes = _gen_dummy_inp_and_dyn_shapes_sdxl(
backbone, min_bs=2, opt_bs=16
)
elif model_id == "sd3-medium":
elif model_id in ["sd3-medium", "sd3.5-medium"]:
dummy_input, dynamic_shapes = _gen_dummy_inp_and_dyn_shapes_sd3(
backbone, min_bs=2, opt_bs=16
)
Expand Down Expand Up @@ -343,14 +352,16 @@ def get_io_shapes(model_id, onnx_load_path, dynamic_shapes):
output_name = "latent"
elif model_id in ["sd3-medium"]:
output_name = "sample"
elif model_id in ["sd3.5-medium"]:
output_name = "out_hidden_states"
elif model_id in ["flux-dev", "flux-schnell"]:
output_name = "output"
else:
raise NotImplementedError(f"Unsupported model_id: {model_id}")

if model_id in ["sdxl-1.0", "sdxl-turbo"]:
io_shapes = {output_name: dynamic_shapes["dynamic_shapes"]["minShapes"]["sample"]}
elif model_id in ["sd3-medium"]:
elif model_id in ["sd3-medium", "sd3.5-medium"]:
io_shapes = {output_name: dynamic_shapes["dynamic_shapes"]["minShapes"]["hidden_states"]}
elif model_id in ["flux-dev", "flux-schnell"]:
io_shapes = {}
Expand Down Expand Up @@ -406,6 +417,9 @@ def modelopt_export_sd(backbone, onnx_dir, model_name, precision):
elif model_name == "sd3-medium":
input_names = ["hidden_states", "encoder_hidden_states", "pooled_projections", "timestep"]
output_names = ["sample"]
elif model_name == "sd3.5-medium":
input_names = ["hidden_states", "encoder_hidden_states", "pooled_projections", "timestep"]
output_names = ["out_hidden_states"]
elif model_name in ["flux-dev", "flux-schnell"]:
input_names = [
"hidden_states",
Expand Down
17 changes: 13 additions & 4 deletions examples/diffusers/quantization/quantize.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@
import argparse
import logging
import sys
import time as time
from collections.abc import Callable
from dataclasses import dataclass
from enum import Enum
Expand Down Expand Up @@ -59,6 +60,7 @@ class ModelType(str, Enum):
SDXL_BASE = "sdxl-1.0"
SDXL_TURBO = "sdxl-turbo"
SD3_MEDIUM = "sd3-medium"
SD35_MEDIUM = "sd3.5-medium"
FLUX_DEV = "flux-dev"
FLUX_SCHNELL = "flux-schnell"
LTX_VIDEO_DEV = "ltx-video-dev"
Expand Down Expand Up @@ -114,6 +116,7 @@ def get_model_filter_func(model_type: ModelType) -> Callable[[str], bool]:
ModelType.SDXL_BASE: filter_func_default,
ModelType.SDXL_TURBO: filter_func_default,
ModelType.SD3_MEDIUM: filter_func_default,
ModelType.SD35_MEDIUM: filter_func_default,
ModelType.LTX_VIDEO_DEV: filter_func_ltx_video,
}

Expand All @@ -125,6 +128,7 @@ def get_model_filter_func(model_type: ModelType) -> Callable[[str], bool]:
ModelType.SDXL_BASE: "stabilityai/stable-diffusion-xl-base-1.0",
ModelType.SDXL_TURBO: "stabilityai/sdxl-turbo",
ModelType.SD3_MEDIUM: "stabilityai/stable-diffusion-3-medium-diffusers",
ModelType.SD35_MEDIUM: "stabilityai/stable-diffusion-3.5-medium",
ModelType.FLUX_DEV: "black-forest-labs/FLUX.1-dev",
ModelType.FLUX_SCHNELL: "black-forest-labs/FLUX.1-schnell",
ModelType.LTX_VIDEO_DEV: "Lightricks/LTX-Video-0.9.7-dev",
Expand Down Expand Up @@ -230,6 +234,7 @@ def uses_transformer(self) -> bool:
"""Check if model uses transformer backbone (vs UNet)."""
return self.model_type in [
ModelType.SD3_MEDIUM,
ModelType.SD35_MEDIUM,
ModelType.FLUX_DEV,
ModelType.FLUX_SCHNELL,
ModelType.LTX_VIDEO_DEV,
Expand Down Expand Up @@ -326,7 +331,7 @@ def create_pipeline_from(
model_id = (
MODEL_REGISTRY[model_type] if override_model_path is None else override_model_path
)
if model_type == ModelType.SD3_MEDIUM:
if model_type in [ModelType.SD3_MEDIUM, ModelType.SD35_MEDIUM]:
pipe = StableDiffusion3Pipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
elif model_type in [ModelType.FLUX_DEV, ModelType.FLUX_SCHNELL]:
pipe = FluxPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
Expand Down Expand Up @@ -357,7 +362,7 @@ def create_pipeline(self) -> DiffusionPipeline:
self.logger.info(f"Data type: {self.config.model_dtype.value}")

try:
if self.config.model_type == ModelType.SD3_MEDIUM:
if self.config.model_type in [ModelType.SD3_MEDIUM, ModelType.SD35_MEDIUM]:
self.pipe = StableDiffusion3Pipeline.from_pretrained(
self.config.model_path, torch_dtype=self.config.torch_dtype
)
Expand Down Expand Up @@ -864,6 +869,8 @@ def main() -> None:
parser = create_argument_parser()
args = parser.parse_args()

s = time.time()

logger = setup_logging(args.verbose)
logger.info("Starting Enhanced Diffusion Model Quantization")

Expand Down Expand Up @@ -939,9 +946,11 @@ def forward_loop(mod):
backbone,
model_config.model_type,
quant_config.format,
quantize_mha=QuantizationConfig.quantize_mha,
quantize_mha=quant_config.quantize_mha,
Copy link
Contributor

@jingyu-ml jingyu-ml Oct 23, 2025

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Thanks for catching the bug here!

)
logger.info(
f"Quantization process completed successfully! Time taken = {time.time() - s} seconds"
)
logger.info("Quantization process completed successfully!")

except Exception as e:
logger.error(f"Quantization failed: {e}", exc_info=True)
Expand Down