File size: 2,294 Bytes
09ceb13 |
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 74 75 76 77 78 79 80 81 |
from huggingface_hub.constants import HF_HUB_CACHE
from transformers import T5EncoderModel, T5TokenizerFast, CLIPTokenizer, CLIPTextModel
import torch
import torch._dynamo
import gc
from PIL import Image as img
from PIL.Image import Image
from pipelines.models import TextToImageRequest
from torch import Generator
from diffusers import FluxTransformer2DModel, DiffusionPipeline
import os
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
Pipeline = None
ckpt_id = "black-forest-labs/FLUX.1-schnell"
ckpt_revision = "741f7c3ce8b383c54771c7003378a50191e9efe9"
def empty_cache():
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
def load_pipeline() -> Pipeline:
empty_cache()
dtype, device = torch.bfloat16, "cuda"
text_encoder_2 = T5EncoderModel.from_pretrained(
"escort321/FLUX.1-schnell1-up",
revision="d7e70e3a8fbc36ec3c47e78913e9c7142bc87b7b",
subfolder="text_encoder_2",
torch_dtype=torch.bfloat16,
)
path = os.path.join(
HF_HUB_CACHE,
"models--escort321--FLUX.1-schnell1-up/snapshots/d7e70e3a8fbc36ec3c47e78913e9c7142bc87b7b/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=dtype,
).to(device)
# quantize_(pipeline.vae, int8_weight_only())
pipeline(
prompt="wordcraft, radiance, ethereal, cartilaginous, tuner, fruity, dullard, existence",
width=1024,
height=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
)
empty_cache()
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]
|