#5.2
from huggingface_hub.constants import HF_HUB_CACHE
from transformers import T5EncoderModel, T5TokenizerFast, CLIPTokenizer, CLIPTextModel
import torch
import torch._dynamo
import gc
import os
from diffusers import FluxPipeline, AutoencoderKL
from PIL.Image import Image
from pipelines.models import TextToImageRequest
from torch import Generator
from diffusers import FluxTransformer2DModel, DiffusionPipeline
from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe
os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True"
os.environ["TOKENIZERS_PARALLELISM"] = "True"
torch._dynamo.config.suppress_errors = True
Pipeline = None
ids = "slobers/Flux.1.Schnella"
Revision = "e34d670e44cecbbc90e4962e7aada2ac5ce8b55b"
def load_pipeline() -> Pipeline:
    path = os.path.join(HF_HUB_CACHE, "models--slobers--Flux.1.Schnella/snapshots/e34d670e44cecbbc90e4962e7aada2ac5ce8b55b/transformer")
    transformer = FluxTransformer2DModel.from_pretrained(path, torch_dtype=torch.bfloat16, use_safetensors=False)
    vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-schnell", revision="741f7c3ce8b383c54771c7003378a50191e9efe9", subfolder="vae", torch_dtype=torch.bfloat16)
    pipeline = FluxPipeline.from_pretrained(ids, revision=Revision, transformer=transformer, vae=vae, local_files_only=True, torch_dtype=torch.bfloat16)
    pipeline.to("cuda")
    pipeline = apply_cache_on_pipe(pipeline, residual_diff_threshold=0.768)
    pipeline("")
    return pipeline
@torch.no_grad()
def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image:
    generator = Generator(pipeline.device).manual_seed(request.seed)
    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]