"ModelTesterMixin",

"MemoryTesterMixin",

"TorchCompileTesterMixin",

# Quantization testers

("BitsAndBytesTesterMixin", "bnb"),

("TorchAoTesterMixin", "torchao"),

("GGUFTesterMixin", "gguf"),

("ModelOptTesterMixin", "modelopt"),

# Quantization compile testers

("BitsAndBytesCompileTesterMixin", "bnb_compile"),

("TorchAoCompileTesterMixin", "torchao_compile"),

("GGUFCompileTesterMixin", "gguf_compile"),

("ModelOptCompileTesterMixin", "modelopt_compile"),

# Cache testers

("PyramidAttentionBroadcastTesterMixin", "pab_cache"),

("FirstBlockCacheTesterMixin", "fbc_cache"),

("FasterCacheTesterMixin", "faster_cache"),

# Other testers

("SingleFileTesterMixin", "single_file"),

("IPAdapterTesterMixin", "ip_adapter"),

("AttentionBackendTesterMixin", "attention_backends"),

("ContextParallelAttentionBackendsTesterMixin", "cp_attn"),

def __init__(self):

self.model_classes = []

self.current_class = None

self.imports = set()

def visit_Import(self, node: ast.Import):

for alias in node.names:

self.imports.add(alias.name.split(".")[-1])

self.generic_visit(node)

def visit_ImportFrom(self, node: ast.ImportFrom):

for alias in node.names:

self.imports.add(alias.name)

self.generic_visit(node)

def visit_ClassDef(self, node: ast.ClassDef):

base_names = []

for base in node.bases:

if isinstance(base, ast.Name):

base_names.append(base.id)

elif isinstance(base, ast.Attribute):

base_names.append(base.attr)

if "ModelMixin" in base_names:

class_info = {

"name": node.name,

"bases": base_names,

"attributes": {},

"has_forward": False,

"init_params": [],

}

for item in node.body:

if isinstance(item, ast.Assign):

for target in item.targets:

if isinstance(target, ast.Name):

attr_name = target.id

if attr_name.startswith("_"):

class_info["attributes"][attr_name] = self._get_value(item.value)

elif isinstance(item, ast.FunctionDef):

if item.name == "forward":

class_info["has_forward"] = True

class_info["forward_params"] = self._extract_func_params(item)

elif item.name == "__init__":

class_info["init_params"] = self._extract_func_params(item)

self.model_classes.append(class_info)

self.generic_visit(node)

def _extract_func_params(self, func_node: ast.FunctionDef) -> list[dict]:

params = []

args = func_node.args

num_defaults = len(args.defaults)

num_args = len(args.args)

first_default_idx = num_args - num_defaults

for i, arg in enumerate(args.args):

if arg.arg == "self":

continue

param_info = {"name": arg.arg, "type": None, "default": None}

if arg.annotation:

param_info["type"] = self._get_annotation_str(arg.annotation)

default_idx = i - first_default_idx

if default_idx >= 0 and default_idx < len(args.defaults):

param_info["default"] = self._get_value(args.defaults[default_idx])

params.append(param_info)

return params

def _get_annotation_str(self, node) -> str:

if isinstance(node, ast.Name):

return node.id

elif isinstance(node, ast.Constant):

return repr(node.value)

elif isinstance(node, ast.Subscript):

base = self._get_annotation_str(node.value)

if isinstance(node.slice, ast.Tuple):

args = ", ".join(self._get_annotation_str(el) for el in node.slice.elts)

else:

args = self._get_annotation_str(node.slice)

return f"{base}[{args}]"

elif isinstance(node, ast.Attribute):

return f"{self._get_annotation_str(node.value)}.{node.attr}"

elif isinstance(node, ast.BinOp) and isinstance(node.op, ast.BitOr):

left = self._get_annotation_str(node.left)

right = self._get_annotation_str(node.right)

return f"{left} | {right}"

elif isinstance(node, ast.Tuple):

return ", ".join(self._get_annotation_str(el) for el in node.elts)

return "Any"

def _get_value(self, node):

if isinstance(node, ast.Constant):

return node.value

elif isinstance(node, ast.Name):

if node.id == "None":

return None

elif node.id == "True":

return True

elif node.id == "False":

return False

return node.id

elif isinstance(node, ast.List):

return [self._get_value(el) for el in node.elts]

elif isinstance(node, ast.Dict):

return {self._get_value(k): self._get_value(v) for k, v in zip(node.keys, node.values)}

with open(filepath) as f:

source = f.read()

tree = ast.parse(source)

analyzer = ModelAnalyzer()

analyzer.visit(tree)

testers = list(ALWAYS_INCLUDE_TESTERS)

for base in model_info["bases"]:

if base in MIXIN_TO_TESTER:

tester = MIXIN_TO_TESTER[base]

if tester not in testers:

testers.append(tester)

for attr, tester in ATTRIBUTE_TO_TESTER.items():

if attr in model_info["attributes"]:

value = model_info["attributes"][attr]

if value is not None and value is not False:

if tester not in testers:

testers.append(tester)

if "_cp_plan" in model_info["attributes"] and model_info["attributes"]["_cp_plan"] is not None:

if "ContextParallelTesterMixin" not in testers:

testers.append("ContextParallelTesterMixin")

# Include AttentionTesterMixin if the model imports attention-related classes

if imports & ATTENTION_INDICATORS:

testers.append("AttentionTesterMixin")

for tester, flag in OPTIONAL_TESTERS:

if flag in include_optional:

if tester == "ContextParallelAttentionBackendsTesterMixin":

if (

"cp_attn" in include_optional

and "_cp_plan" in model_info["attributes"]

and model_info["attributes"]["_cp_plan"] is not None

):

testers.append(tester)

elif tester not in testers:

testers.append(tester)

class_name = f"{model_name}TesterConfig"

model_class = model_info["name"]

forward_params = model_info.get("forward_params", [])

init_params = model_info.get("init_params", [])

lines = [

f"class {class_name}:",

" @property",

" def model_class(self):",

f" return {model_class}",

"",

" @property",

" def pretrained_model_name_or_path(self):",

' return "" # TODO: Set Hub repository ID',

"",

" @property",

" def pretrained_model_kwargs(self):",

' return {"subfolder": "transformer"}',

"",

" @property",

" def generator(self):",

' return torch.Generator("cpu").manual_seed(0)',

"",

" def get_init_dict(self) -> dict[str, int | list[int]]:",

]

if init_params:

lines.append(" # __init__ parameters:")

for param in init_params:

type_str = f": {param['type']}" if param["type"] else ""

default_str = f" = {param['default']}" if param["default"] is not None else ""

lines.append(f" # {param['name']}{type_str}{default_str}")

lines.extend(

[

" return {}",

"",

" def get_dummy_inputs(self) -> dict[str, torch.Tensor]:",

]

)

if forward_params:

lines.append(" # forward() parameters:")

for param in forward_params:

type_str = f": {param['type']}" if param["type"] else ""

default_str = f" = {param['default']}" if param["default"] is not None else ""

lines.append(f" # {param['name']}{type_str}{default_str}")

lines.extend(

[

" # TODO: Fill in dummy inputs",

" return {}",

"",

" @property",

" def input_shape(self) -> tuple[int, ...]:",

" return (1, 1)",

"",

" @property",

" def output_shape(self) -> tuple[int, ...]:",

" return (1, 1)",

]

)

tester_short = tester.replace("TesterMixin", "")

class_name = f"Test{model_name}{tester_short}"

lines = [f"class {class_name}({config_class}, {tester}):"]

if tester == "TorchCompileTesterMixin":

lines.extend(

[

" @property",

" def different_shapes_for_compilation(self):",

" return [(4, 4), (4, 8), (8, 8)]",

"",

" def get_dummy_inputs(self, height: int = 4, width: int = 4) -> dict[str, torch.Tensor]:",

" # TODO: Implement dynamic input generation",

" return {}",

]

)

elif tester == "IPAdapterTesterMixin":

lines.extend(

[

" @property",

" def ip_adapter_processor_cls(self):",

" return None # TODO: Set processor class",

"",

" def modify_inputs_for_ip_adapter(self, model, inputs_dict):",

" # TODO: Add IP adapter image embeds to inputs",

" return inputs_dict",

"",

" def create_ip_adapter_state_dict(self, model):",

" # TODO: Create IP adapter state dict",

" return {}",

]

)

elif tester == "SingleFileTesterMixin":

lines.extend(

[

" @property",

" def ckpt_path(self):",

' return "" # TODO: Set checkpoint path',

"",

" @property",

" def alternate_ckpt_paths(self):",

" return []",

"",

" @property",

" def pretrained_model_name_or_path(self):",

' return "" # TODO: Set Hub repository ID',

]

)

elif tester == "GGUFTesterMixin":

lines.extend(

[

" @property",

" def gguf_filename(self):",

' return "" # TODO: Set GGUF filename',

"",

" def get_dummy_inputs(self) -> dict[str, torch.Tensor]:",

" # TODO: Override with larger inputs for quantization tests",

" return {}",

]

)

elif tester in ["BitsAndBytesTesterMixin", "TorchAoTesterMixin", "ModelOptTesterMixin"]:

lines.extend(

[

" def get_dummy_inputs(self) -> dict[str, torch.Tensor]:",

" # TODO: Override with larger inputs for quantization tests",

" return {}",

]

)

elif tester in [

"BitsAndBytesCompileTesterMixin",

"TorchAoCompileTesterMixin",

"ModelOptCompileTesterMixin",

]:

lines.extend(

[

" def get_dummy_inputs(self) -> dict[str, torch.Tensor]:",

" # TODO: Override with larger inputs for quantization compile tests",

" return {}",

]

)

elif tester == "GGUFCompileTesterMixin":

lines.extend(

[

" @property",

" def gguf_filename(self):",

' return "" # TODO: Set GGUF filename',

"",

" def get_dummy_inputs(self) -> dict[str, torch.Tensor]:",

" # TODO: Override with larger inputs for quantization compile tests",

" return {}",

]

)

elif tester in [

"PyramidAttentionBroadcastTesterMixin",

"FirstBlockCacheTesterMixin",

"FasterCacheTesterMixin",

]:

lines.append(" pass")

elif tester == "LoraHotSwappingForModelTesterMixin":

lines.extend(

[

" @property",

" def different_shapes_for_compilation(self):",

" return [(4, 4), (4, 8), (8, 8)]",

"",

" def get_dummy_inputs(self, height: int = 4, width: int = 4) -> dict[str, torch.Tensor]:",

" # TODO: Implement dynamic input generation",

" return {}",

]

)

else:

lines.append(" pass")

model_name = model_info["name"].replace("2DModel", "").replace("3DModel", "").replace("Model", "")

testers = determine_testers(model_info, include_optional, imports)

tester_imports = set(testers)

if "LoraTesterMixin" in testers:

tester_imports.add("LoraHotSwappingForModelTesterMixin")

lines = [

"# coding=utf-8",

"# Copyright 2026 HuggingFace Inc.",

"#",

'# Licensed under the Apache License, Version 2.0 (the "License");',

"# you may not use this file except in compliance with the License.",

"# You may obtain a copy of the License at",

"#",

"# http://www.apache.org/licenses/LICENSE-2.0",

"#",

"# Unless required by applicable law or agreed to in writing, software",

'# distributed under the License is distributed on an "AS IS" BASIS,',

"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.",

"# See the License for the specific language governing permissions and",

"# limitations under the License.",

"",

"import torch",

"",

f"from diffusers import {model_info['name']}",

"from diffusers.utils.torch_utils import randn_tensor",

"",

"from ...testing_utils import enable_full_determinism, torch_device",

]

lines.extend(

[

"from ..testing_utils import (",

*[f" {tester}," for tester in sorted(tester_imports)],

")",

"",

"",

"enable_full_determinism()",

"",

"",

]

)

config_class = f"{model_name}TesterConfig"

lines.append(generate_config_class(model_info, model_name))

lines.append("")

lines.append("")

for tester in testers:

lines.append(generate_test_class(model_name, config_class, tester))

lines.append("")

lines.append("")

if "LoraTesterMixin" in testers:

lines.append(generate_test_class(model_name, config_class, "LoraHotSwappingForModelTesterMixin"))

lines.append("")

lines.append("")

path = Path(model_filepath)

model_filename = path.stem

if "transformers" in path.parts:

return f"tests/models/transformers/test_models_{model_filename}.py"

elif "unets" in path.parts:

return f"tests/models/unets/test_models_{model_filename}.py"

elif "autoencoders" in path.parts:

return f"tests/models/autoencoders/test_models_{model_filename}.py"

else:

parser = argparse.ArgumentParser(description="Generate test suite for a diffusers model class")

parser.add_argument(

"model_filepath",

type=str,

help="Path to the model file (e.g., src/diffusers/models/transformers/transformer_flux.py)",

)

parser.add_argument(

"--output", "-o", type=str, default=None, help="Output file path (default: auto-generated based on model path)"

)

parser.add_argument(

"--include",

"-i",

type=str,

nargs="*",

default=[],

choices=[

"bnb",

"torchao",

"gguf",

"modelopt",

"bnb_compile",

"torchao_compile",

"gguf_compile",

"modelopt_compile",

"pab_cache",

"fbc_cache",

"faster_cache",

"single_file",

"ip_adapter",

"attention_backends",

"cp_attn",

"all",

],

help="Optional testers to include",

)

parser.add_argument(

"--class-name",

"-c",

type=str,

default=None,

help="Specific model class to generate tests for (default: first model class found)",

)

parser.add_argument("--dry-run", action="store_true", help="Print generated code without writing to file")

args = parser.parse_args()

if not Path(args.model_filepath).exists():

print(f"Error: File not found: {args.model_filepath}", file=sys.stderr)

sys.exit(1)

model_classes, imports = analyze_model_file(args.model_filepath)

if not model_classes:

print(f"Error: No model classes found in {args.model_filepath}", file=sys.stderr)

sys.exit(1)

if args.class_name:

model_info = next((m for m in model_classes if m["name"] == args.class_name), None)

if not model_info:

available = [m["name"] for m in model_classes]

print(f"Error: Class '{args.class_name}' not found. Available: {available}", file=sys.stderr)

sys.exit(1)

else:

model_info = model_classes[0]

if len(model_classes) > 1:

print(f"Multiple model classes found, using: {model_info['name']}", file=sys.stderr)

print("Use --class-name to specify a different class", file=sys.stderr)

include_optional = args.include

if "all" in include_optional:

include_optional = [flag for _, flag in OPTIONAL_TESTERS]

generated_code = generate_test_file(model_info, args.model_filepath, include_optional, imports)

if args.dry_run:

print(generated_code)

else:

output_path = args.output or get_test_output_path(args.model_filepath)

output_dir = Path(output_path).parent

output_dir.mkdir(parents=True, exist_ok=True)

with open(output_path, "w") as f:

f.write(generated_code)

print(f"Generated test file: {output_path}")

print(f"Model class: {model_info['name']}")