|
|
from diffusers import FluxPipeline, AutoencoderKL, AutoencoderTiny |
|
|
from diffusers.image_processor import VaeImageProcessor |
|
|
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler |
|
|
from huggingface_hub.constants import HF_HUB_CACHE |
|
|
from transformers import T5EncoderModel, T5TokenizerFast, CLIPTokenizer, CLIPTextModel |
|
|
import torch |
|
|
import torch._dynamo |
|
|
import gc |
|
|
from PIL import Image as img |
|
|
from PIL.Image import Image |
|
|
from pipelines.models import TextToImageRequest |
|
|
from torch import Generator |
|
|
import time |
|
|
from diffusers import FluxTransformer2DModel, DiffusionPipeline |
|
|
from torchao.quantization import quantize_, int8_weight_only, fpx_weight_only |
|
|
import os |
|
|
|
|
|
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True" |
|
|
torch._dynamo.config.suppress_errors = True |
|
|
|
|
|
Pipeline = None |
|
|
|
|
|
ckpt_id = "black-forest-labs/FLUX.1-schnell" |
|
|
ckpt_revision = "741f7c3ce8b383c54771c7003378a50191e9efe9" |
|
|
|
|
|
|
|
|
def empty_cache(): |
|
|
gc.collect() |
|
|
torch.cuda.empty_cache() |
|
|
torch.cuda.reset_max_memory_allocated() |
|
|
torch.cuda.reset_peak_memory_stats() |
|
|
|
|
|
|
|
|
def load_text_encoder_2(): |
|
|
id = "city96/t5-v1_1-xxl-encoder-bf16" |
|
|
revision = "1b9c856aadb864af93c1dcdc226c2774fa67bc86" |
|
|
|
|
|
return T5EncoderModel.from_pretrained( |
|
|
id, |
|
|
revision=revision, |
|
|
torch_dtype=torch.bfloat16, |
|
|
).to(memory_format=torch.channels_last) |
|
|
|
|
|
|
|
|
def load_transformer_model(): |
|
|
id = "position025/FLUX.1-schnell-8-pda" |
|
|
revision = "cdee1db6a8b1858435ec4397b19ed703265d2153" |
|
|
|
|
|
transformer_path = os.path.join( |
|
|
HF_HUB_CACHE, |
|
|
f"models--{id.split('/')[0]}--{id.split('/')[1]}/snapshots/{revision}", |
|
|
) |
|
|
return FluxTransformer2DModel.from_pretrained( |
|
|
transformer_path, torch_dtype=torch.bfloat16, use_safetensors=False |
|
|
).to(memory_format=torch.channels_last) |
|
|
|
|
|
|
|
|
def load_pipeline() -> Pipeline: |
|
|
empty_cache() |
|
|
|
|
|
text_encoder_2 = load_text_encoder_2() |
|
|
transformer_model = load_transformer_model() |
|
|
|
|
|
pipeline = FluxPipeline.from_pretrained( |
|
|
ckpt_id, |
|
|
revision=ckpt_revision, |
|
|
transformer=transformer_model, |
|
|
text_encoder_2=text_encoder_2, |
|
|
torch_dtype=torch.bfloat16, |
|
|
).to("cuda") |
|
|
|
|
|
pipeline.transformer = torch.compile(pipeline.transformer, mode="reduce-overhead") |
|
|
for _ in range(3): |
|
|
pipeline( |
|
|
prompt="divination, aftermath, airy, flatworm, adjuster, fleshy, dunce, presence", |
|
|
width=1024, |
|
|
height=1024, |
|
|
guidance_scale=0.0, |
|
|
num_inference_steps=4, |
|
|
max_sequence_length=256, |
|
|
) |
|
|
|
|
|
empty_cache() |
|
|
return pipeline |
|
|
|
|
|
|
|
|
@torch.no_grad() |
|
|
def infer( |
|
|
request: TextToImageRequest, pipeline: Pipeline, generator: Generator |
|
|
) -> Image: |
|
|
return pipeline( |
|
|
request.prompt, |
|
|
generator=generator, |
|
|
guidance_scale=0.0, |
|
|
num_inference_steps=4, |
|
|
max_sequence_length=256, |
|
|
height=request.height, |
|
|
width=request.width, |
|
|
output_type="pil", |
|
|
).images[0] |
|
|
|
