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