File size: 2,673 Bytes
0ed3f4c | 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 | import gc
import os
from typing import TypeAlias
import torch
from PIL.Image import Image
from diffusers import (
FluxPipeline,
FluxTransformer2DModel,
AutoencoderKL,
AutoencoderTiny,
DiffusionPipeline,
)
from huggingface_hub.constants import HF_HUB_CACHE
from pipelines.models import TextToImageRequest
from torch import Generator
from torchao.quantization import quantize_, int8_weight_only
from transformers import T5EncoderModel, CLIPTextModel, logging
import torch._dynamo
torch._dynamo.config.suppress_errors = True
Pipeline: TypeAlias = FluxPipeline
torch.backends.cudnn.benchmark = True
torch._inductor.config.conv_1x1_as_mm = True
torch._inductor.config.coordinate_descent_tuning = True
torch._inductor.config.epilogue_fusion = False
torch._inductor.config.coordinate_descent_check_all_directions = True
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
os.environ["TOKENIZERS_PARALLELISM"] = "True"
CHECKPOINT = "jokerbit/flux.1-schnell-Robert-int8wo"
REVISION = "5ef0012f11a863e5111ec56540302a023bc8587b"
def load_pipeline() -> Pipeline:
path = os.path.join(
HF_HUB_CACHE,
"models--jokerbit--flux.1-schnell-Robert-int8wo/snapshots/5ef0012f11a863e5111ec56540302a023bc8587b/transformer",
)
transformer = FluxTransformer2DModel.from_pretrained(
path, use_safetensors=False, local_files_only=True, torch_dtype=torch.bfloat16
)
pipeline = FluxPipeline.from_pretrained(
CHECKPOINT,
revision=REVISION,
transformer=transformer,
local_files_only=True,
torch_dtype=torch.bfloat16,
).to("cuda")
pipeline.to(memory_format=torch.channels_last)
pipeline.transformer = torch.compile(pipeline.transformer, mode="max-autotune")
quantize_(pipeline.vae, int8_weight_only())
pipeline.vae = torch.compile(pipeline.vae, mode="max-autotune")
with torch.no_grad():
for _ in range(5):
pipeline(
prompt="onomancy, aftergo, spirantic, Platyhelmia, modificator, drupaceous, jobbernowl, hereness",
width=1024,
height=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
)
torch.cuda.empty_cache()
return pipeline
@torch.no_grad()
def infer(
request: TextToImageRequest, pipeline: Pipeline, generator: torch.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,
).images[0]
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