File size: 2,294 Bytes
afa07d5 | 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 | import torch
import torch._dynamo
import os
import torch.nn.functional as F
from PIL import Image
from pipelines.models import TextToImageRequest
from torch import Generator
from typing import Type
from diffusers import DiffusionPipeline, FluxTransformer2DModel
from huggingface_hub.constants import HF_HUB_CACHE
from transformers import T5EncoderModel
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
os.environ["TOKENIZERS_PARALLELISM"] = "True"
torch._dynamo.config.suppress_errors = True
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.enabled = True
Pipeline = None
def load_pipeline() -> Pipeline:
ckpt_id = "black-forest-labs/FLUX.1-schnell"
ckpt_revision = "741f7c3ce8b383c54771c7003378a50191e9efe9"
text_encoder_2 = T5EncoderModel.from_pretrained(
"strong943/autoencoder-tiny",
revision="33e36134bd12b626986cfc1fee662a82976c6d24",
subfolder="text_encoder_2",
torch_dtype=torch.bfloat16,
)
path = os.path.join(
HF_HUB_CACHE,
"models--strong943--autoencoder-tiny/snapshots/33e36134bd12b626986cfc1fee662a82976c6d24/transformer",
)
transformer = FluxTransformer2DModel.from_pretrained(
path, torch_dtype=torch.bfloat16, use_safetensors=False
)
pipeline = DiffusionPipeline.from_pretrained(
ckpt_id,
revision=ckpt_revision,
transformer=transformer,
text_encoder_2=text_encoder_2,
torch_dtype=torch.bfloat16,
)
pipeline.to("cuda")
pipeline.to(memory_format=torch.channels_last)
with torch.inference_mode():
pipeline(
prompt="oblivious, drumlet, earthen, bioelectric, radiograph, kinesis, subcortical, cytoplasmic",
width=1024,
height=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
)
return pipeline
@torch.no_grad()
def infer(
request: TextToImageRequest, pipeline: Pipeline, generator: 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,
output_type="pil",
).images[0]
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