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9f818c5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 | # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: OpenMDW-1.1
import asyncio
import base64
import json
import math
import mimetypes
import shutil
from pathlib import Path
from typing import TYPE_CHECKING, Annotated, Any
import openai
import pydantic
import tyro
from tqdm import tqdm
from cosmos_framework.inference.args import ModelMode, OmniSampleArgs, OmniSampleOverrides, OmniSetupOverrides
from cosmos_framework.model.vfm.upsampler.prompts import build_messages, clean_response
from cosmos_framework.utils import log
if TYPE_CHECKING:
from cosmos_framework.configs.base.defaults.model_config import OmniMoTModelConfig
_PACKAGE_DIR = Path(__file__).parents[1].absolute()
class PromptUpsamplerArgs(pydantic.BaseModel):
endpoint_url: str = "http://localhost:8000/v1"
"""The URL of the API server."""
model: str | None = None
"""The model to use.
If not provided, the first model in the list will be used.
"""
debug: bool = False
"""If True, save raw API responses for debugging."""
max_workers: int = 16
"""Maximum number of concurrent requests to the API."""
max_retries: int = 5
"""Maximum number of retries for each request."""
class Args(pydantic.BaseModel):
input_files: Annotated[list[Path], tyro.conf.arg(aliases=("-i",))]
"""Path to the input sample argument files."""
# output_dir: Annotated[Path, tyro.conf.arg(aliases=("-o",))]
# """Output directory."""
setup: tyro.conf.OmitArgPrefixes[OmniSetupOverrides] = OmniSetupOverrides.model_construct()
"""Setup arguments."""
prompt_upsampler: PromptUpsamplerArgs = PromptUpsamplerArgs.model_construct()
"""Prompt upsampler arguments."""
class Sample(pydantic.BaseModel):
overrides: OmniSampleOverrides
args: OmniSampleArgs
messages: list
_TASKS = {
ModelMode.TEXT2IMAGE: "t2i",
ModelMode.TEXT2VIDEO: "t2v",
ModelMode.IMAGE2VIDEO: "i2v",
}
def _dump_json(obj: Any, path: Path):
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(json.dumps(obj, indent=2, sort_keys=True))
def _model_dump_json(obj: pydantic.BaseModel, path: Path, **kwargs):
_dump_json(obj.model_dump(mode="json", **kwargs), path)
async def _process_sample(
args: Args,
client: openai.AsyncOpenAI,
sample: Sample,
):
assert args.prompt_upsampler.model
for i_retry in range(args.prompt_upsampler.max_retries):
msg_prefix = f"['{sample.args.name}'|{i_retry + 1}]"
# Send request
try:
response = await client.chat.completions.create(
model=args.prompt_upsampler.model,
messages=sample.messages,
seed=i_retry,
max_tokens=20000,
temperature=0.7,
top_p=0.8,
presence_penalty=1.5,
extra_body={"top_k": 20, "min_p": 0.0},
)
except Exception as e:
log.warning(f"{msg_prefix} API Error: {e}")
await asyncio.sleep(1) # Backoff before retrying
continue
if args.prompt_upsampler.debug:
retry_dir = sample.args.output_dir / f"{i_retry}"
retry_dir.mkdir(parents=True, exist_ok=True)
_model_dump_json(response, retry_dir / "prompt_upsampler_response.json")
assert len(response.choices) == 1
choice = response.choices[0]
if choice.finish_reason != "stop" or not choice.message.content:
log.warning(f"{msg_prefix} Invalid response: {choice.finish_reason}")
continue
# Extract final prompt
text = choice.message.content.strip()
text, info = clean_response(text)
text = text.removeprefix("```json\n").removesuffix("```")
try:
prompt_json = json.loads(text)
except json.JSONDecodeError as e:
log.warning(f"{msg_prefix} Invalid JSON response: {e}")
continue
if not isinstance(prompt_json, dict):
log.warning(f"{msg_prefix} Invalid JSON type: {type(prompt_json)}")
continue
if not prompt_json.get("scene_imagination"):
log.warning(f"{msg_prefix} Empty JSON response")
continue
prompt = json.dumps(prompt_json)
sample_overrides = sample.overrides.model_copy(
update={
"prompt": prompt,
"prompt_path": None,
}
)
_model_dump_json(sample_overrides, Path(f"{sample.args.output_dir}.json"), exclude_none=True)
return
log.warning(f"['{sample.args.name}'] Failed to get response")
async def process_sample(
args: Args,
client: openai.AsyncOpenAI,
semaphore: asyncio.Semaphore,
sample: Sample,
):
async with semaphore:
return await _process_sample(args, client, sample)
async def upsample_prompts(args: Args):
setup_args = args.setup.build_setup()
sample_overrides_list = OmniSampleOverrides.from_files(args.input_files, overrides=setup_args.sample_overrides)
if not sample_overrides_list:
raise ValueError(f"No samples found for {args.input_files}")
log.info(f"Loaded {len(sample_overrides_list)} samples")
model_config: "OmniMoTModelConfig" = setup_args.load_model_config().config
# Build samples
samples: list[Sample] = []
for sample_overrides in sample_overrides_list:
assert sample_overrides.name
raw_sample_overrides = sample_overrides.model_copy(deep=True)
sample_overrides.output_dir = setup_args.output_dir / sample_overrides.name
if sample_overrides.sample_meta.model_mode not in _TASKS:
log.info(f"Skipping '{sample_overrides.name}'")
_model_dump_json(raw_sample_overrides, Path(f"{sample_overrides.output_dir}.json"), exclude_none=True)
continue
sample_overrides.download(sample_overrides.output_dir / "inputs")
sample_args = sample_overrides.build_sample(model_config=model_config)
is_video = sample_args.num_frames > 1
messages = build_messages(
task=_TASKS[sample_args.model_mode],
description=sample_args.prompt,
aspect_ratio=str(sample_args.aspect_ratio),
resolution_w=sample_args.vision_size[0],
resolution_h=sample_args.vision_size[1],
fps=sample_args.fps if is_video else None,
duration_secs=math.ceil(sample_args.duration) if is_video else None,
)
assert len(messages) == 2 and messages[1]["role"] == "user"
user_message = messages[1]
user_content = [
{"type": "text", "text": user_message.pop("content")},
]
if sample_args.vision_path:
vision_url = str(sample_args.vision_path)
if "://" not in vision_url:
vision_url = _base64_encode(sample_args.vision_path)
user_content.insert(0, {"type": "image_url", "image_url": {"url": vision_url}})
user_message["content"] = user_content
sample = Sample(args=sample_args, overrides=raw_sample_overrides, messages=messages)
if args.prompt_upsampler.debug:
_model_dump_json(sample.args, sample.args.output_dir / "sample_args.json")
_dump_json(sample.messages, sample.args.output_dir / "prompt_upsampler_messages.json")
else:
shutil.rmtree(sample.args.output_dir, ignore_errors=True)
samples.append(sample)
client = openai.AsyncOpenAI(
api_key="EMPTY",
base_url=args.prompt_upsampler.endpoint_url,
timeout=3600,
)
if not args.prompt_upsampler.model:
models = await client.models.list()
args.prompt_upsampler.model = models.data[0].id
log.info(f"Using model: {args.prompt_upsampler.model}")
# Process samples
semaphore = asyncio.Semaphore(args.prompt_upsampler.max_workers)
tasks = [
process_sample(
args=args,
client=client,
semaphore=semaphore,
sample=sample,
)
for sample in samples
]
for result in tqdm(asyncio.as_completed(tasks), desc="Upsampling", total=len(samples)):
await result
def _base64_encode(path: Path) -> str:
mime_type = mimetypes.guess_type(str(path))[0] or "image/png"
encoded = base64.b64encode(path.read_bytes()).decode("ascii")
return f"data:{mime_type};base64,{encoded}"
def main():
args = tyro.cli(Args, description=__doc__)
asyncio.run(upsample_prompts(args))
if __name__ == "__main__":
main()
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