283 lines
12 KiB
Python
283 lines
12 KiB
Python
#!/usr/bin/env python3
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"""Private FLUX.2 Klein 9B FP8 beta worker for Athena's two GPUs.
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The FP8 diffusion transformer runs on the RTX 5080. A Qwen3-8B NF4 text
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encoder runs on the RTX 3060 while the profile controller temporarily pauses
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Qwen3-TTS. The transformer and encoder are released before VAE decoding so
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the 1024px decoder has sufficient workspace on the RTX 5080.
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"""
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from __future__ import annotations
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import gc
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import json
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import os
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import signal
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import time
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from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
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from pathlib import Path
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from types import MethodType
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HOST = os.environ.get("WORKER_HOST", "0.0.0.0")
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PORT = int(os.environ.get("WORKER_PORT", "8086"))
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TOKEN = os.environ.get("WORKER_TOKEN", "").strip()
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COMPONENT_DIR = os.environ.get("FLUX_COMPONENT_DIR", "/models/components")
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TRANSFORMER_FILE = os.environ.get(
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"FLUX_TRANSFORMER_FILE", "/models/fp8/flux-2-klein-9b-fp8.safetensors")
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OUTPUT_DIR = Path(os.environ.get("IMAGE_DIR", "/data/images")).resolve()
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ACTIVE = False
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os.environ.setdefault("DIFFUSERS_VERBOSITY", "error")
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os.environ.setdefault("TRANSFORMERS_VERBOSITY", "error")
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os.environ.setdefault("HF_HUB_DISABLE_PROGRESS_BARS", "1")
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os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
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if len(TOKEN) < 32:
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raise RuntimeError("WORKER_TOKEN is missing or too short")
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signal.signal(signal.SIGTERM, lambda *_: os._exit(0))
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def _devices(torch):
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if torch.cuda.device_count() != 2:
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raise RuntimeError("FLUX 9B beta requires exactly two visible CUDA GPUs")
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totals = {i: torch.cuda.get_device_properties(i).total_memory
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for i in range(torch.cuda.device_count())}
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transformer_index = max(totals, key=totals.get)
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encoder_index = min(totals, key=totals.get)
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return (transformer_index, encoder_index,
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torch.device(f"cuda:{transformer_index}"),
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torch.device(f"cuda:{encoder_index}"))
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def _install_fp8_converter():
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import diffusers.loaders.single_file_model as single_file_model
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original = single_file_model.SINGLE_FILE_LOADABLE_CLASSES[
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"Flux2Transformer2DModel"]["checkpoint_mapping_fn"]
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scales = {}
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double_map = {
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"img_attn.proj": "attn.to_out.0",
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"img_mlp.0": "ff.linear_in",
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"img_mlp.2": "ff.linear_out",
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"txt_attn.proj": "attn.to_add_out",
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"txt_mlp.0": "ff_context.linear_in",
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"txt_mlp.2": "ff_context.linear_out",
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}
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single_map = {
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"linear1": "attn.to_qkv_mlp_proj",
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"linear2": "attn.to_out",
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}
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def record(key, value):
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parts = key.split(".")
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scale_name, block = parts[-1], parts[1]
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within = ".".join(parts[2:-1])
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if parts[0] == "double_blocks":
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if within == "img_attn.qkv":
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targets = ("attn.to_q", "attn.to_k", "attn.to_v")
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elif within == "txt_attn.qkv":
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targets = ("attn.add_q_proj", "attn.add_k_proj",
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"attn.add_v_proj")
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else:
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targets = (double_map[within],)
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prefix = f"transformer_blocks.{block}"
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elif parts[0] == "single_blocks":
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targets = (single_map[within],)
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prefix = f"single_transformer_blocks.{block}"
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else:
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raise ValueError(f"unexpected FP8 scale key: {key}")
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for target in targets:
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scales.setdefault(f"{prefix}.{target}", {})[scale_name] = value.clone()
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def convert(checkpoint, **kwargs):
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scales.clear()
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for key in list(checkpoint):
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if key.endswith((".input_scale", ".weight_scale")):
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record(key, checkpoint.pop(key))
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return original(checkpoint=checkpoint, **kwargs)
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single_file_model.SINGLE_FILE_LOADABLE_CLASSES[
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"Flux2Transformer2DModel"]["checkpoint_mapping_fn"] = convert
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return scales
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def _fp8_forward(torch, module, inputs):
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shape = inputs.shape
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input_fp8 = ((inputs / module._fp8_input_scale)
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.clamp(torch.finfo(torch.float8_e4m3fn).min,
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torch.finfo(torch.float8_e4m3fn).max)
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.to(torch.float8_e4m3fn).reshape(-1, shape[-1]))
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output = torch._scaled_mm(
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input_fp8,
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module.weight.reshape(-1, module.weight.shape[-1]).t(),
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scale_a=module._fp8_input_scale,
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scale_b=module._fp8_weight_scale,
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bias=module.bias,
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out_dtype=inputs.dtype,
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use_fast_accum=True,
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)
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return output.reshape(*shape[:-1], output.shape[-1])
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def generate(data: dict) -> dict:
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global ACTIVE
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import torch
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from diffusers import (Flux2KleinPipeline, Flux2Transformer2DModel,
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NVIDIAModelOptConfig)
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from modelopt.torch.opt import enable_huggingface_checkpointing
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from modelopt.torch.quantization.config import FP8_DEFAULT_CFG
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from PIL import Image
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from transformers import BitsAndBytesConfig, Qwen3ForCausalLM
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prompt, filename = data.get("prompt"), data.get("filename")
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if not isinstance(prompt, str) or not prompt.strip() or len(prompt) > 8000:
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raise ValueError("invalid prompt")
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if (not isinstance(filename, str) or Path(filename).name != filename
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or not filename.endswith(".png")):
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raise ValueError("invalid filename")
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width, height = int(data.get("width", 1024)), int(data.get("height", 1024))
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if (width, height) != (1024, 1024):
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raise ValueError("FLUX 9B beta currently supports only 1024x1024")
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if int(data.get("steps", 4)) != 4 or float(data.get("guidance", 1.0)) != 1.0:
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raise ValueError("FLUX 9B beta requires steps=4 and guidance=1.0")
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source_files = data.get("source_files") or []
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if not isinstance(source_files, list) or len(source_files) > 4:
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raise ValueError("invalid source image list")
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source_images = []
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for name in source_files:
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if not isinstance(name, str) or Path(name).name != name:
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raise ValueError("invalid source image filename")
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source = (OUTPUT_DIR / name).resolve()
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if source.parent != OUTPUT_DIR or not source.is_file():
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raise ValueError("source image not found")
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with Image.open(source) as opened:
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source_images.append(opened.convert("RGB"))
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started = time.monotonic()
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ACTIVE = True
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transformer = text_encoder = pipe = latent = decoded = image = None
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try:
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enable_huggingface_checkpointing()
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scales = _install_fp8_converter()
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tx_index, enc_index, tx_device, enc_device = _devices(torch)
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quantization = NVIDIAModelOptConfig(
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quant_type="FP8", weight_only=False,
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modelopt_config=FP8_DEFAULT_CFG)
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transformer = Flux2Transformer2DModel.from_single_file(
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TRANSFORMER_FILE, config=COMPONENT_DIR, subfolder="transformer",
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quantization_config=quantization, torch_dtype=torch.bfloat16,
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device_map={"": tx_index}, local_files_only=True)
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patched = 0
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for module_name, module in transformer.named_modules():
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if module_name not in scales:
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continue
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module.register_buffer("_fp8_input_scale",
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scales[module_name]["input_scale"])
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module.register_buffer("_fp8_weight_scale",
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scales[module_name]["weight_scale"])
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module.forward = MethodType(
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lambda self, inputs: _fp8_forward(torch, self, inputs), module)
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patched += 1
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if patched != len(scales):
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raise RuntimeError(f"patched only {patched} of {len(scales)} FP8 layers")
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transformer.to(tx_device)
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text_encoder = Qwen3ForCausalLM.from_pretrained(
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os.path.join(COMPONENT_DIR, "text_encoder"),
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torch_dtype=torch.bfloat16, low_cpu_mem_usage=True,
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quantization_config=BitsAndBytesConfig(
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load_in_4bit=True, bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_use_double_quant=True),
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device_map={"": enc_index}, local_files_only=True)
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pipe = Flux2KleinPipeline.from_pretrained(
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COMPONENT_DIR, transformer=transformer, text_encoder=text_encoder,
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torch_dtype=torch.bfloat16, local_files_only=True)
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pipe.vae.enable_slicing()
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pipe.vae.enable_tiling()
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pipe.vae.to(tx_device)
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loaded = time.monotonic() - started
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prompt_embeds, _ = pipe.encode_prompt(
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prompt.strip(), device=enc_device, max_sequence_length=128)
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prompt_embeds = prompt_embeds.to(tx_device)
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pipe.text_encoder = None
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seed = data.get("seed")
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generator = None if seed is None else torch.Generator(
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device=tx_device).manual_seed(int(seed))
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kwargs = {
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"prompt": None, "prompt_embeds": prompt_embeds,
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"height": height, "width": width, "num_inference_steps": 4,
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"guidance_scale": 1.0, "generator": generator,
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"output_type": "latent",
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}
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if source_images:
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kwargs["image"] = (source_images[0] if len(source_images) == 1
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else source_images)
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latent = pipe(**kwargs).images
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pipe.transformer = None
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del transformer, text_encoder, prompt_embeds, generator
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transformer = text_encoder = None
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gc.collect()
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torch.cuda.empty_cache()
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latent = latent.to(device=tx_device, dtype=pipe.vae.dtype)
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decoded = pipe.vae.decode(latent, return_dict=False)[0]
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image = pipe.image_processor.postprocess(
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decoded.detach(), output_type="pil")[0]
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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image.save(OUTPUT_DIR / filename)
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return {"status": "ok", "filename": filename,
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"seconds": round(time.monotonic() - started, 3),
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"load_seconds": round(loaded, 3),
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"model": "FLUX.2-klein-9B-fp8-beta"}
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finally:
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for value in (image, decoded, latent, pipe, text_encoder, transformer):
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if value is not None:
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del value
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gc.collect()
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torch.cuda.empty_cache()
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ACTIVE = False
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class Handler(BaseHTTPRequestHandler):
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def log_message(self, fmt: str, *args: object) -> None:
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print(f"[flux9b-beta] {self.client_address[0]} {fmt % args}", flush=True)
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def reply(self, status: int, payload: dict) -> None:
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body = json.dumps(payload, separators=(",", ":")).encode()
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self.send_response(status)
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self.send_header("Content-Type", "application/json")
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self.send_header("Content-Length", str(len(body)))
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self.end_headers()
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self.wfile.write(body)
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def do_GET(self) -> None: # noqa: N802
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if self.path == "/health":
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self.reply(200, {"status": "ok", "model_loaded": ACTIVE,
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"model": "FLUX.2-klein-9B-fp8-beta"})
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else:
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self.reply(404, {"error": "not found"})
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def do_POST(self) -> None: # noqa: N802
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if self.headers.get("Authorization", "") != f"Bearer {TOKEN}":
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self.reply(401, {"error": "unauthorized"})
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return
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if self.path != "/generate":
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self.reply(404, {"error": "not found"})
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return
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try:
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length = int(self.headers.get("Content-Length", "0"))
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if length < 2 or length > 16384:
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raise ValueError("invalid request size")
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self.reply(200, generate(json.loads(self.rfile.read(length))))
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except Exception as exc:
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print(f"[flux9b-beta] generation failed: {type(exc).__name__}: "
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f"{str(exc)[:1000]}", flush=True)
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self.reply(500, {"status": "error", "message": str(exc)})
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ThreadingHTTPServer((HOST, PORT), Handler).serve_forever()
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