| """Generate with Image 2.1 Calibrated FP8. Built with Qwen.""" |
| import argparse, json, time |
| from pathlib import Path |
| import torch |
| from fp8_runtime import load_pipeline |
|
|
| @torch.inference_mode() |
| def main(): |
| ap=argparse.ArgumentParser() |
| inputs=ap.add_mutually_exclusive_group(required=True) |
| inputs.add_argument('--prompt') |
| inputs.add_argument('--prompts-json', help='JSON list of prompt strings; one loaded pipeline serves the whole list') |
| ap.add_argument('--base',default='Qwen/Qwen-Image-2.1') |
| ap.add_argument('--quant',default=str(Path(__file__).parent/'transformer')) |
| ap.add_argument('--width',type=int,default=2048) |
| ap.add_argument('--height',type=int,default=2048) |
| ap.add_argument('--steps',type=int,default=40) |
| ap.add_argument('--seed',type=int,default=42) |
| ap.add_argument('--output',default='output.png') |
| ap.add_argument('--image', nargs='+', action='extend', metavar='PATH', |
| help='One or more local reference images for editing, in image 1, image 2, ... order. ' |
| 'Pass multiple paths or repeat --image. Omit for text-to-image generation.') |
| ap.add_argument('--transparent', action='store_true', |
| help='Request native transparent RGBA output and require a PNG filename') |
| ap.add_argument('--eager', action='store_true', help='Use the original uncompiled runtime') |
| ap.add_argument('--warmup', action='store_true', help='Run a full untimed warmup; report its cost separately') |
| ap.add_argument('--cache-prompts', action='store_true', |
| help='Reuse exact encoder outputs for repeated text-only prompts in this process') |
| args=ap.parse_args() |
| prompts=json.loads(Path(args.prompts_json).read_text()) if args.prompts_json else [args.prompt] |
| if not isinstance(prompts,list) or not prompts or not all(isinstance(p,str) and p for p in prompts): |
| ap.error('prompts-json must contain a nonempty list of prompt strings') |
| if args.transparent: |
| if Path(args.output).suffix.lower() != '.png': |
| ap.error('--transparent requires --output ending in .png to preserve the alpha channel') |
| from transparency import transparent_prompt |
| prompts = [transparent_prompt(p) for p in prompts] |
| kw={} |
| if args.image: |
| from PIL import Image |
| reference_images=[] |
| for filename in args.image: |
| try: |
| with Image.open(filename) as reference: |
| reference_images.append(reference.copy()) |
| except (OSError, ValueError) as exc: |
| ap.error(f'cannot read --image {filename!r}: {exc}') |
| kw['image']=reference_images[0] if len(reference_images)==1 else reference_images |
| start=time.perf_counter();pipe=load_pipeline(args.base,args.quant) |
| if not args.eager: |
| from acceleration import accelerate_pipeline |
| accelerate_pipeline(pipe) |
| if args.cache_prompts: |
| from prompt_cache import cache_prompt_embeddings |
| cache_prompt_embeddings(pipe) |
| torch.cuda.synchronize();load_seconds=time.perf_counter()-start |
| def run(prompt,seed): |
| return pipe(prompt=prompt,width=args.width,height=args.height,num_inference_steps=args.steps, |
| generator=torch.Generator('cuda').manual_seed(seed),**kw).images[0] |
| warmup_seconds=0 |
| if args.warmup: |
| start=time.perf_counter();run(prompts[0],args.seed);torch.cuda.synchronize() |
| warmup_seconds=time.perf_counter()-start |
| path=Path(args.output);path.parent.mkdir(parents=True,exist_ok=True) |
| for i,prompt in enumerate(prompts): |
| torch.cuda.synchronize();start=time.perf_counter();result=run(prompt,args.seed+i) |
| torch.cuda.synchronize();seconds=time.perf_counter()-start |
| destination=path if len(prompts)==1 else path.with_name(f'{path.stem}-{i:03d}{path.suffix or ".png"}') |
| result.save(destination) |
| from transparency import alpha_statistics |
| print(json.dumps({'seconds':seconds,'output':str(destination),'width':result.width,'height':result.height, |
| 'steps':args.steps,'eager':args.eager,'load_seconds':load_seconds, |
| 'input_image_count':len(args.image or []), |
| 'transparent_requested':args.transparent,**alpha_statistics(result), |
| 'prompt_cache':getattr(pipe,'_image21_prompt_cache_stats',None), |
| 'warmup_seconds':warmup_seconds, |
| 'timing_note':'Generation includes any compilation on this call; excludes model load and PNG writing.'}),flush=True) |
|
|
| if __name__=='__main__':main() |
|
|
