| from diffusers import FluxPipeline, AutoencoderKL, AutoencoderTiny |
| from diffusers.schedulers import FlowMatchEulerDiscreteScheduler |
|
|
| from transformers import T5EncoderModel, T5TokenizerFast, CLIPTokenizer, CLIPTextModel |
| import torch |
| import torch._dynamo |
| import gc |
| from PIL import Image as img |
| from PIL import Image |
| from pipelines.models import TextToImageRequest |
| from torch import Generator |
| import time |
| from diffusers import FluxTransformer2DModel, DiffusionPipeline |
| from torchao.quantization import quantize_, int8_weight_only |
| from diffusers.image_processor import VaeImageProcessor |
| Pipeline = None |
| import os |
| MODEL_ID = "black-forest-labs/FLUX.1-schnell" |
| traced_vae_decode_path = "traced_vae_decode.pt" |
| def empty_cache(): |
| start = time.time() |
| gc.collect() |
| torch.cuda.empty_cache() |
| torch.cuda.reset_max_memory_allocated() |
| torch.cuda.reset_peak_memory_stats() |
| print(f"Flush took: {time.time() - start}") |
|
|
|
|
|
|
| def load_pipeline() -> Pipeline: |
| empty_cache() |
| dtype, device = torch.bfloat16, "cuda" |
| vae = AutoencoderKL.from_pretrained( |
| MODEL_ID, subfolder="vae", torch_dtype=torch.bfloat16 |
| ) |
| quantize_(vae, int8_weight_only()) |
| pipeline = DiffusionPipeline.from_pretrained( |
| MODEL_ID, |
| vae=vae, |
| torch_dtype=dtype, |
| ) |
| |
| pipeline.enable_sequential_cpu_offload() |
| for _ in range(2): |
| empty_cache() |
| pipeline(prompt="onomancy, aftergo, spirantic, Platyhelmia, modificator, drupaceous, jobbernowl, hereness", width=1024, height=1024, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256) |
| |
| return pipeline |
|
|
| def trace_and_save_vae_decoder(vae, latents): |
| try: |
| traced_vae_decode = torch.jit.trace(vae.decode, (latents, True)) |
| torch.jit.save(traced_vae_decode, traced_vae_decode_path) |
| return traced_vae_decode |
| except Exception as e: |
| print(f"JIT tracing failed: {e}") |
| return vae.decode |
| |
| def decode_latents_to_image(latents, height: int, width: int, vae): |
| if not height: |
| height = 1024 |
| if not width: |
| width = 1024 |
| if vae.config.block_out_channels: |
| vae_scale_factor = 2 ** (len(vae.config.block_out_channels)) |
| else: |
| vae_scale_factor = 1 |
| image_processor = VaeImageProcessor(vae_scale_factor=vae_scale_factor) |
|
|
| traced_vae_decode = vae.decode |
| with torch.no_grad(): |
| latents = FluxPipeline._unpack_latents(latents.unsqueeze(0), height, width, vae_scale_factor) |
| latents = (latents / vae.config.scaling_factor) + vae.config.shift_factor |
| image = traced_vae_decode(latents, return_dict=False)[0] |
| decoded_image = image_processor.postprocess(image, output_type="pil")[0] |
| |
| return decoded_image |
|
|
|
|
| @torch.inference_mode() |
| def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image: |
| empty_cache() |
| generator = Generator("cuda").manual_seed(request.seed) |
| latent=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="latent").images[0] |
| return decode_latents_to_image(latent, request.height, request.width, pipeline.vae) |
