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import os
#os.environ["CUDA_VISIBLE_DEVICES"] = "6"
from typing import Dict, List, Any
import torch
from torch import autocast
from diffusers import FluxPipeline, AutoPipelineForText2Image, FluxTransformer2DModel, BitsAndBytesConfig
import base64
from io import BytesIO
from huggingface_hub import login
token = os.getenv("HF_TOKEN")
if token is None:
raise ValueError("The environment variable HF_TOKEN is not set")
login(token=token)
# set device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if device.type != 'cuda':
raise ValueError("need to run on GPU")
class EndpointHandler():
def __init__(self, path=""):
# First load and merge LoRA weights
ckpt_id = "black-forest-labs/FLUX.1-dev"
fused_transformer_path = os.path.join(path, "fused_transformer")
if not os.path.exists(fused_transformer_path):
print("Fusing LoRA weights into transformer...")
pipeline = FluxPipeline.from_pretrained(
ckpt_id,
text_encoder=None,
text_encoder_2=None,
torch_dtype=torch.float16
)
pipeline.load_lora_weights(path, weight_name="pytorch_lora_weights.safetensors")
pipeline.fuse_lora()
pipeline.unload_lora_weights()
pipeline.transformer.save_pretrained(fused_transformer_path)
print("Finished fusing LoRA weights")
# Load quantized model
bnb_4bit_compute_dtype = torch.float16
nf4_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=bnb_4bit_compute_dtype,
)
transformer = FluxTransformer2DModel.from_pretrained(
os.path.join(path, "fused_transformer"),
quantization_config=nf4_config,
torch_dtype=bnb_4bit_compute_dtype,
)
self.pipe = AutoPipelineForText2Image.from_pretrained(
ckpt_id,
transformer=transformer,
torch_dtype=bnb_4bit_compute_dtype
)
self.pipe.enable_model_cpu_offload()
def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
"""
Args:
data (:obj:):
includes the input data and the parameters for the inference.
Return:
A :obj:`dict`:. base64 encoded image
"""
inputs = data.pop("inputs", data)
# run inference pipeline
with autocast(device.type):
image = self.pipe(
prompt=inputs,
num_inference_steps=28,
generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'),
width=1024,
height=1024,
guidance_scale=3.5,
).images[0]
#os.makedirs('generated_images', exist_ok=True)
#image_path = f'generated_images/generated_{hash(inputs)}.jpg'
#image.save(image_path)
return image
# encode image as base 64
#buffered = BytesIO()
#image.save(buffered, format="JPEG")
# img_str = base64.b64encode(buffered.getvalue())
# postprocess the prediction
#return {"image": img_str.decode()} |