| from diffusers import AutoencoderKL |
| from diffusers.image_processor import VaeImageProcessor |
| import torch |
| import torch._dynamo |
| import gc |
| from PIL import Image |
| from pipelines.models import TextToImageRequest |
| from torch import Generator |
| from diffusers import DiffusionPipeline |
| from torchao.quantization import quantize_, int8_weight_only |
|
|
| Pipeline = None |
| MODEL_ID = "black-forest-labs/FLUX.1-schnell" |
| def clear(): |
| gc.collect() |
| torch.cuda.empty_cache() |
| torch.cuda.reset_max_memory_allocated() |
| torch.cuda.reset_peak_memory_stats() |
|
|
| def load_pipeline() -> Pipeline: |
| clear() |
| dtype, device = torch.bfloat16, "cuda" |
| vae = AutoencoderKL.from_pretrained( |
| MODEL_ID, subfolder="vae", torch_dtype=torch.bfloat16 |
| ) |
| quantize_(vae, int8_weight_only(), device="cuda") |
| pipeline = DiffusionPipeline.from_pretrained( |
| MODEL_ID, |
| vae=vae, |
| torch_dtype=dtype, |
| ) |
| pipeline.enable_sequential_cpu_offload() |
| pipeline(prompt="unpervaded, unencumber, froggish, groundneedle, transnatural, fatherhood, outjump, cinerator", width=1024, height=1024, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256) |
| clear() |
| return pipeline |
|
|
| @torch.inference_mode() |
| def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image: |
| clear() |
| if request.seed is None: |
| generator = None |
| else: |
| generator = Generator(device="cuda").manual_seed(request.seed) |
|
|
| image=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] |
| return image |
