AxonAI-77dedd / src /pipeline.py
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import os
import gc
import torch
import torch._dynamo
from typing import TypeAlias
from torch import Generator
from PIL.Image import Image
from diffusers import (
FluxPipeline,
FluxTransformer2DModel,
AutoencoderTiny,
DiffusionPipeline,
)
from huggingface_hub.constants import HF_HUB_CACHE
from pipelines.models import TextToImageRequest
from torchao.quantization import quantize_, int8_weight_only
from transformers import T5EncoderModel
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 = "winner632/flux1-schnell-int8wo"
REVISION = "d9ff2fc9ad81476d3ef3a5f40d273f0fa5a36f2b"
def clear_gpu_cache():
"""Frees GPU memory to prevent memory leaks."""
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
def load_pipeline() -> Pipeline:
"""Loads the diffusion pipeline with quantization and optimizations."""
clear_gpu_cache()
transformer_model = FluxTransformer2DModel.from_pretrained(
os.path.join(
HF_HUB_CACHE,
"models--winner632--flux1-schnell-int8wo/snapshots/d9ff2fc9ad81476d3ef3a5f40d273f0fa5a36f2b/transformer",
),
use_safetensors=True,
local_files_only=True,
torch_dtype=torch.bfloat16,
)
pipe = FluxPipeline.from_pretrained(
CHECKPOINT,
revision=REVISION,
transformer=transformer_model,
local_files_only=True,
torch_dtype=torch.bfloat16,
).to("cuda")
pipe.to(memory_format=torch.channels_last)
pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead")
quantize_(pipe.transformer, int8_weight_only())
quantize_(pipe.vae, int8_weight_only())
pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead")
with torch.no_grad():
for _ in range(5):
pipe(
prompt="onomancy, aftergo, spirantic, Platyhelmia, modificator, drupaceous, jobbernowl, hereness",
width=1024,
height=1024,
guidance_scale=0,
num_inference_steps=4,
max_sequence_length=256,
)
clear_gpu_cache()
return pipe
@torch.no_grad()
def infer(
request: TextToImageRequest, pipeline: Pipeline, generator: Generator
) -> Image:
"""Generates an image from text input using the loaded pipeline."""
return pipeline(
request.prompt,
generator=generator,
guidance_scale=0e0,
num_inference_steps=4,
max_sequence_length=256,
height=request.height,
width=request.width,
output_type="pil",
).images[0]
# Example Usage
if __name__ == "__main__":
print("load pipeline...")
diffusion_pipeline = load_pipeline()
sample_request = TextToImageRequest(
prompt="A futuristic cityscape with neon lights",
height=1024,
width=1024,
)
generator = torch.Generator(device="cuda").manual_seed(42)
print("Generating image...")
generated_img = infer(sample_request, diffusion_pipeline, generator)
generated_img.show()