Datasets:
File size: 6,660 Bytes
9bc4c0e | 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 | """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)
# Inspect every age group's neutral image early, then prioritize visible
# expressions. Only accepted neutral pixels are reused from the first pilot.
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()
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