| """Reference-expression pilot alongside background generation on one shared GPU.""" |
| import hashlib |
| import io |
| import json |
| import os |
| from pathlib import Path |
| import tarfile |
| import time |
| from datetime import datetime,timezone |
| import torch |
| from PIL import Image |
| from native_flux_inference import create_native_pipeline, generate_native_image, encode_text |
| from background_production import add_member,dump,BASE_REV,LORA_REV |
|
|
| ROOT=Path(__file__).resolve().parents[1] |
| STATE=ROOT/'experiments/character-production' |
| BUFFER=Path('/tmp/vivarium-character-production') |
| LORA_SHA='977642f4d7427fa3203b5b3b3c3a4ef9cb72186c59bd71b63e870ff53bfa6451' |
|
|
| def now():return datetime.now(timezone.utc).isoformat() |
|
|
| def metadata(person,expression,image_bytes,reference_sha,seconds,encode_seconds,shard,profile): |
| record={k:v for k,v in person.items() if k!='expressions'} |
| record.update(expression) |
| record.update(dict(schema_version='1.0',generated_utc=now(),image_sha256=hashlib.sha256(image_bytes).hexdigest(), |
| reference_image_sha256=reference_sha,reference_resize=dict(width=416 if reference_sha else 0,height=624 if reference_sha else 0,resample='PIL BICUBIC' if reference_sha else ''), |
| inference=dict(model='black-forest-labs/FLUX.2-klein-base-9B',revision=BASE_REV, |
| lora='laion/FLUX.2-klein-base-9B-vivarium-loras',lora_revision=LORA_REV,lora_subfolder='characters', |
| lora_sha256=LORA_SHA,lora_rank=64,lora_alpha=64,lora_training_step=1500,lora_scale=1.0, |
| steps=50,guidance_scale=4.0,seed=expression['seed'],width=832,height=1248,dtype='bfloat16', |
| quantization=False,distillation=False,max_sequence_length=256,cfg_mode='batched',lora_fused=True, |
| qkv_fused=True,compile_mode='default',negative_embedding_cached=True,positive_embedding_cached=True), |
| runtime=dict(profile=profile,gpu=torch.cuda.get_device_name(0),physical_gpu=int(os.environ.get('CUDA_VISIBLE_DEVICES','0')), |
| batch_size=1,generation_seconds=seconds,positive_text_encoding_seconds=encode_seconds, |
| torch=torch.__version__,cuda=torch.version.cuda,includes_compile_warmup=False, |
| gpu_shared_with_backgrounds=profile.startswith('shared'),model_cpu_offload=profile.startswith('shared')), |
| storage=dict(shard=shard,image_member=expression['id']+'.png',json_member=expression['id']+'.json',prompt_member=expression['id']+'.txt',encoding='PNG RGB'))) |
| return record |
|
|
| def main(): |
| BUFFER.mkdir(parents=True,exist_ok=True) |
| plan=json.loads((STATE/'person-plan.json').read_text()) |
| ids=['person-0002','person-0120','person-0253'] |
| people=[next(p for p in plan if p['personality_id']==i) for i in ids] |
| dump(STATE/'pilot-state.json',dict(status='loading CPU-offloaded pipeline',ids=ids,utc=now())) |
| pipe=create_native_pipeline('characters',device='cpu',compile_mode='default',cfg_mode='batched') |
| pipe.enable_model_cpu_offload(gpu_id=0) |
| cache={};encode_times={} |
| for p in people: |
| for e in p['expressions']: |
| start=time.perf_counter();cache[e['id']]=encode_text(pipe,e['prompt']).cpu();torch.cuda.synchronize() |
| encode_times[e['id']]=time.perf_counter()-start |
| neg=p['expressions'][0]['negative_prompt'] |
| if neg not in pipe._native_text_cache:pipe._native_text_cache[neg]=encode_text(pipe,neg) |
| dump(STATE/'pilot-state.json',dict(status='generating',ids=ids,preencoded_prompts=len(cache),utc=now())) |
| all_records=[];references={};reference_shas={};archives={} |
| for p in people: |
| name='pilot-'+p['personality_id']+'.tar' |
| archives[p['personality_id']]=tarfile.open(BUFFER/(name+'.partial'),'w',format=tarfile.USTAR_FORMAT) |
| |
| |
| order=['neutral','angry','joyful','sad','afraid','disgust','pain','annoyed','grateful','playful'] |
| first_generated=True |
| for emotion in order: |
| for p in people: |
| ident=p['personality_id'];e=next(x for x in p['expressions'] if x['emotion']==emotion) |
| name='pilot-'+ident+'.tar';tar=archives[ident];bootstrap=None |
| if ident=='person-0002' and emotion=='neutral': |
| prior=list((BUFFER/'pilot-first-pass').glob('pilot-person-0002.tar*')) |
| if prior: |
| with tarfile.open(prior[0],'r:') as old: |
| png_member=old.next();png=old.extractfile(png_member).read() |
| json_member=old.next();bootstrap=json.loads(old.extractfile(json_member).read()) |
| assert bootstrap['id']==e['id'] and bootstrap['prompt']==e['prompt'] |
| with Image.open(io.BytesIO(png)) as im:image=im.convert('RGB') |
| m=bootstrap |
| if bootstrap is None: |
| torch.cuda.synchronize();start=time.perf_counter() |
| image=generate_native_image(pipe,prompt_embeds=cache[e['id']].to(pipe._execution_device),seed=e['seed'], |
| steps=50,guidance=4.,negative_prompt=e['negative_prompt'],image=references.get(ident) if emotion!='neutral' else None, |
| width=832,height=1248)[0] |
| torch.cuda.synchronize();seconds=time.perf_counter()-start |
| encoded=io.BytesIO();image.save(encoded,format='PNG',compress_level=4);png=encoded.getvalue() |
| m=metadata(p,e,png,reference_shas.get(ident,''),seconds,encode_times[e['id']],name,'shared-GPU7-CPU-offload-preencoded-pilot') |
| m['runtime']['includes_compile_warmup']=first_generated |
| first_generated=False |
| add_member(tar,e['id']+'.png',png);add_member(tar,e['id']+'.json',json.dumps(m,indent=2,ensure_ascii=False).encode());add_member(tar,e['id']+'.txt',e['prompt'].encode()) |
| tar.fileobj.flush() |
| all_records.append(m) |
| if emotion=='neutral': |
| references[ident]=image.resize((416,624),Image.Resampling.BICUBIC);reference_shas[ident]=hashlib.sha256(png).hexdigest() |
| print(json.dumps(dict(id=e['id'],seconds=round(m['runtime']['generation_seconds'],2))),flush=True) |
| dump(STATE/'pilot-records.json',all_records) |
| dump(STATE/'pilot-state.json',dict(status='generating',images_completed=len(all_records),latest=e['id'],utc=now())) |
| for ident,tar in archives.items(): |
| tar.close();name='pilot-'+ident+'.tar';(BUFFER/(name+'.partial')).replace(BUFFER/name) |
| dump(STATE/'pilot-state.json',dict(status='complete',images_completed=len(all_records),ids=ids,utc=now())) |
|
|
| if __name__=='__main__':main() |
|
|
