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#!/usr/bin/env python3
import torch
from diffusers import FluxPipeline, DPMSolverMultistepScheduler
from BeamDiffusionModel.models.diffusionModel.configs.config_loader import CONFIG
from functools import partial
from BeamDiffusionModel.models.diffusionModel.Latents_Singleton import Latents
class Flux:
def __init__(self):
self.device = "cuda" if CONFIG.get("flux", {}).get("use_cuda", True) and torch.cuda.is_available() else "cpu"
self.torch_dtype = torch.bfloat16 if CONFIG.get("flux", {}).get("precision") == "bfloat16" else torch.float16
print(f"Loading model: {CONFIG['flux']['id']} on {self.device}")
self.pipe = FluxPipeline.from_pretrained(CONFIG["flux"]["id"], torch_dtype=torch.bfloat16)
self.pipe.enable_sequential_cpu_offload()
self.pipe.vae.enable_slicing()
self.pipe.vae.enable_tiling()
self.pipe.tokenizer.truncation_side = 'left'
print("Model loaded successfully!")
def capture_latents(self, latents_store: Latents, pipe, step, timestep, callback_kwargs):
latents = callback_kwargs["latents"]
latents_store.add_latents(latents)
return callback_kwargs
def generate_image(self, prompt: str, latent=None, generator=None):
latents = Latents()
callback = partial(self.capture_latents, latents)
img = self.pipe(prompt, latents=latent, callback_on_step_end=callback,
generator=generator, callback_on_step_end_tensor_inputs=["latents"],
height=768,
width=768,
guidance_scale=3.5,
max_sequence_length=512,
num_inference_steps=CONFIG["flux"]["diffusion_settings"]["steps"]).images[0]
return img, latents.dump_and_clear() |