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import torch
from huggingface_guess import model_list
from backend import memory_management
from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
from backend.modules.k_prediction import PredictionDiscreteFlow
from backend.patcher.clip import CLIP
from backend.patcher.unet import UnetPatcher
from backend.patcher.vae import VAE
from backend.text_processing.gemma_engine import GemmaTextProcessingEngine
class Lumina2(ForgeDiffusionEngine):
matched_guesses = [model_list.Lumina2]
def __init__(self, estimated_config, huggingface_components):
super().__init__(estimated_config, huggingface_components)
self.is_inpaint = False
clip = CLIP(model_dict={"gemma2": huggingface_components["text_encoder"]}, tokenizer_dict={"gemma2": huggingface_components["tokenizer"]})
vae = VAE(model=huggingface_components["vae"])
k_predictor = PredictionDiscreteFlow(estimated_config)
unet = UnetPatcher.from_model(model=huggingface_components["transformer"], diffusers_scheduler=None, k_predictor=k_predictor, config=estimated_config)
self.text_processing_engine_gemma = GemmaTextProcessingEngine(
text_encoder=clip.cond_stage_model.gemma2,
tokenizer=clip.tokenizer.gemma2,
)
self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
self.forge_objects_original = self.forge_objects.shallow_copy()
self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
self.use_shift = True
self.is_flux = True
@torch.inference_mode()
def get_learned_conditioning(self, prompt: list[str]):
memory_management.load_model_gpu(self.forge_objects.clip.patcher)
shift = getattr(prompt, "distilled_cfg_scale", 6.0)
self.forge_objects.unet.model.predictor.set_parameters(shift=shift)
return self.text_processing_engine_gemma(prompt)
@torch.inference_mode()
def get_prompt_lengths_on_ui(self, prompt):
token_count = len(self.text_processing_engine_gemma.tokenize([prompt])[0])
return token_count, max(999, token_count)
@torch.inference_mode()
def encode_first_stage(self, x):
sample = self.forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5)
sample = self.forge_objects.vae.first_stage_model.process_in(sample)
return sample.to(x)
@torch.inference_mode()
def decode_first_stage(self, x):
sample = self.forge_objects.vae.first_stage_model.process_out(x)
sample = self.forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0
return sample.to(x)
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