File size: 8,395 Bytes
fdafd05 | 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 | """CLI for standalone agentic Cosmos3 text-to-image prompt upsampling."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
from agentic_upsampling.clients import (
ImageGenerationClient,
PromptRewriterClient,
VLMQualityJudge,
read_api_token,
read_optional_generation_auth_key,
)
from agentic_upsampling.constants import (
DEFAULT_ASPECT_RATIO,
DEFAULT_CRITIC_ENDPOINT_URL,
DEFAULT_CRITIC_MODEL,
DEFAULT_FLOW_SHIFT,
DEFAULT_GENERATION_AUTH_KEY_ENV,
DEFAULT_GENERATION_EXTRA_ARGS,
DEFAULT_GENERATION_MODEL,
DEFAULT_GEMINI_API_KEY_ENV,
DEFAULT_GUIDANCE,
DEFAULT_IMAGE_SIZE,
DEFAULT_LLM_EXTRA_BODY,
DEFAULT_MAX_ITERATIONS,
DEFAULT_NUM_STEPS,
DEFAULT_OPENAI_API_KEY_ENV,
DEFAULT_RESOLUTION,
DEFAULT_REWRITER_ENDPOINT_URL,
DEFAULT_REWRITER_MODEL,
DEFAULT_SAMPLES_PER_ITERATION,
DEFAULT_UPSAMPLER_ENDPOINT_URL,
DEFAULT_UPSAMPLER_MODEL,
)
from agentic_upsampling.data import load_prompt_items
from agentic_upsampling.extract_best import extract_best_images
from agentic_upsampling.io_utils import write_json_atomic
from agentic_upsampling.runner import AgenticUpsamplerRunner, RunnerConfig, write_run_manifest
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
input_group = parser.add_mutually_exclusive_group(required=True)
input_group.add_argument("--prompt", default=None, help="Single text prompt to run.")
input_group.add_argument("--prompts", type=Path, default=None, help="Path to .txt, .jsonl, or .csv prompts.")
parser.add_argument("--limit", type=int, default=None, help="Optional maximum number of prompts to run.")
parser.add_argument("--output-dir", type=Path, required=True)
parser.add_argument("--overwrite", action="store_true")
parser.add_argument("--max-iterations", type=int, default=DEFAULT_MAX_ITERATIONS)
parser.add_argument("--samples-per-iteration", type=int, default=DEFAULT_SAMPLES_PER_ITERATION)
parser.add_argument("--seed-base", type=int, default=None)
parser.add_argument("--disable-early-stop", action="store_true")
parser.add_argument("--quiet", action="store_true")
parser.add_argument("--extract-best", action="store_true", help="Copy best images after the run finishes.")
parser.add_argument("--generation-endpoint", required=True)
parser.add_argument("--generation-model", default=DEFAULT_GENERATION_MODEL)
parser.add_argument("--size", default=DEFAULT_IMAGE_SIZE, help="vLLM-Omni image size in WIDTHxHEIGHT format.")
parser.add_argument("--generation-auth-key", default="")
parser.add_argument("--generation-auth-key-env", default=DEFAULT_GENERATION_AUTH_KEY_ENV)
parser.add_argument("--resolution", default=DEFAULT_RESOLUTION)
parser.add_argument("--aspect-ratio", default=DEFAULT_ASPECT_RATIO)
parser.add_argument("--num-steps", type=int, default=DEFAULT_NUM_STEPS)
parser.add_argument("--guidance", type=float, default=DEFAULT_GUIDANCE)
parser.add_argument("--flow-shift", type=float, default=DEFAULT_FLOW_SHIFT)
parser.add_argument("--generation-extra-args", type=json.loads, default=DEFAULT_GENERATION_EXTRA_ARGS)
parser.add_argument("--upsampler-endpoint-url", default=DEFAULT_UPSAMPLER_ENDPOINT_URL)
parser.add_argument("--upsampler-model", default=DEFAULT_UPSAMPLER_MODEL)
parser.add_argument("--rewriter-endpoint-url", default=DEFAULT_REWRITER_ENDPOINT_URL)
parser.add_argument("--rewriter-model", default=DEFAULT_REWRITER_MODEL)
parser.add_argument("--openai-api-key-env", default=DEFAULT_OPENAI_API_KEY_ENV)
parser.add_argument("--openai-api-key-file", type=Path, default=None)
parser.add_argument("--llm-extra-body", type=json.loads, default=DEFAULT_LLM_EXTRA_BODY)
parser.add_argument("--initial-negative-prompt", default="")
parser.add_argument("--critic-endpoint-url", default=DEFAULT_CRITIC_ENDPOINT_URL)
parser.add_argument("--critic-model", default=DEFAULT_CRITIC_MODEL)
parser.add_argument("--gemini-api-key-env", default=DEFAULT_GEMINI_API_KEY_ENV)
parser.add_argument("--gemini-api-key-file", type=Path, default=None)
return parser.parse_args()
def main() -> int:
args = parse_args()
args.output_dir.mkdir(parents=True, exist_ok=True)
items = load_prompt_items(prompt=args.prompt, prompts_path=args.prompts, limit=args.limit)
if not items:
raise RuntimeError("No prompts selected.")
if args.samples_per_iteration < 1:
raise ValueError("--samples-per-iteration must be >= 1.")
if not isinstance(args.generation_extra_args, dict):
raise ValueError("--generation-extra-args must decode to a JSON object.")
openai_token = read_api_token(args.openai_api_key_env, args.openai_api_key_file)
gemini_token = read_api_token(args.gemini_api_key_env, args.gemini_api_key_file)
generation_auth_key = read_optional_generation_auth_key(args.generation_auth_key, args.generation_auth_key_env)
write_json_atomic(
args.output_dir / "run_config.json",
{
"selected_prompts": len(items),
"max_iterations": args.max_iterations,
"samples_per_iteration": args.samples_per_iteration,
"early_stop": not args.disable_early_stop,
"generation_endpoint": args.generation_endpoint,
"generation_model": args.generation_model,
"size": args.size,
"resolution": args.resolution,
"aspect_ratio": args.aspect_ratio,
"num_steps": args.num_steps,
"guidance": args.guidance,
"flow_shift": args.flow_shift,
"generation_extra_args": args.generation_extra_args,
"upsampler_endpoint_url": args.upsampler_endpoint_url,
"upsampler_model": args.upsampler_model,
"rewriter_endpoint_url": args.rewriter_endpoint_url,
"rewriter_model": args.rewriter_model,
"llm_extra_body": args.llm_extra_body,
"critic_endpoint_url": args.critic_endpoint_url,
"critic_model": args.critic_model,
"initial_negative_prompt": args.initial_negative_prompt,
},
)
rewriter = PromptRewriterClient(
api_token=openai_token,
upsampler_endpoint_url=args.upsampler_endpoint_url,
upsampler_model=args.upsampler_model,
rewriter_endpoint_url=args.rewriter_endpoint_url,
rewriter_model=args.rewriter_model,
extra_body=args.llm_extra_body,
resolution=args.resolution,
aspect_ratio=args.aspect_ratio,
)
generator = ImageGenerationClient(
endpoint=args.generation_endpoint,
auth_key=generation_auth_key,
model=args.generation_model,
size=args.size,
num_steps=args.num_steps,
guidance=args.guidance,
flow_shift=args.flow_shift,
extra_args=args.generation_extra_args,
)
judge = VLMQualityJudge(
api_token=gemini_token,
endpoint_url=args.critic_endpoint_url,
model=args.critic_model,
)
runner = AgenticUpsamplerRunner(
rewriter=rewriter,
generator=generator,
judge=judge,
config=RunnerConfig(
output_dir=args.output_dir,
max_iterations=args.max_iterations,
samples_per_iteration=args.samples_per_iteration,
overwrite=args.overwrite,
seed_base=args.seed_base,
initial_negative_prompt=args.initial_negative_prompt,
early_stop=not args.disable_early_stop,
verbose=not args.quiet,
),
)
results = [runner.run_item_safely(item) for item in items]
write_run_manifest(args.output_dir, results)
failures = sum(1 for item in results if item.get("error"))
summary = {"selected_prompts": len(items), "completed": len(items) - failures, "failures": failures}
write_json_atomic(args.output_dir / "summary.json", summary)
print(json.dumps(summary, indent=2), flush=True)
if args.extract_best and not failures:
export_dir = args.output_dir / "best_generations"
extract_best_images(args.output_dir, export_dir, overwrite=args.overwrite)
print(f"Exported best images to {export_dir}", flush=True)
return 1 if failures else 0
if __name__ == "__main__":
raise SystemExit(main())
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