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5bc51fb | 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 | from huggingface_hub.constants import HF_HUB_CACHE
from diffusers import FluxPipeline
from PIL.Image import Image
from pipelines.models import TextToImageRequest
from torch import Generator
from diffusers import FluxTransformer2DModel
import torch
import torch._dynamo
import gc
import os
os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True"
os.environ["TOKENIZERS_PARALLELISM"] = "True"
torch._dynamo.config.suppress_errors = True
Pipeline = None
base_prompt = "insensible, timbale, pothery, electrovital, actinogram, taxis, intracerebellar, centrodesmus"
def load_pipeline() -> Pipeline:
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
transformer = FluxTransformer2DModel.from_pretrained(os.path.join(HF_HUB_CACHE, "models--fringuant--StreamCascade/snapshots/765016449ab8494685f030a7db03c67600cf4c55/transformer"), torch_dtype=torch.bfloat16, use_safetensors=False)
pipeline = FluxPipeline.from_pretrained("fringuant/StreamCascade", revision="765016449ab8494685f030a7db03c67600cf4c55", transformer=transformer, local_files_only=True, torch_dtype=torch.bfloat16,)
pipeline.to("cuda")
pipeline.vae = torch.compile(pipeline.vae, mode="max-autotune", fullgraph=True, dynamic=True)
for idx in range(3):
pipeline(prompt=base_prompt, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=4, max_sequence_length=256)
return pipeline
@torch.no_grad()
def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image:
prompt = getattr(request, 'prompt', base_prompt)
return pipeline(
prompt,
generator=Generator(pipeline.device).manual_seed(request.seed),
guidance_scale=6.5,
num_inference_steps=4,
max_sequence_length=256,
height=request.height,
width=request.width,
).images[0]
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