mirror of
https://github.com/lllyasviel/stable-diffusion-webui-forge.git
synced 2026-07-21 21:01:24 +08:00
81 lines
3.1 KiB
Python
81 lines
3.1 KiB
Python
from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from modules.prompt_parser import SdConditioning
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import torch
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from huggingface_guess import model_list
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from backend import memory_management
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from backend.args import dynamic_args
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from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
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from backend.modules.k_prediction import PredictionFlux2
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from backend.patcher.clip import CLIP
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from backend.patcher.unet import UnetPatcher
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from backend.patcher.vae import VAE
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from backend.text_processing.klein_engine import KleinTextProcessingEngine
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from modules.shared import opts
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class Flux2(ForgeDiffusionEngine):
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matched_guesses = [model_list.Flux2K4B, model_list.Flux2K9B]
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def __init__(self, estimated_config, huggingface_components):
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super().__init__(estimated_config, huggingface_components)
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clip = CLIP(model_dict={"qwen3": huggingface_components["text_encoder"]}, tokenizer_dict={"qwen3": huggingface_components["tokenizer"]})
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vae = VAE(model=huggingface_components["vae"], is_flux2=True)
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k_predictor = PredictionFlux2(estimated_config)
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unet = UnetPatcher.from_model(model=huggingface_components["transformer"], diffusers_scheduler=None, k_predictor=k_predictor, config=estimated_config)
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self.text_processing_engine_gemma = KleinTextProcessingEngine(
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text_encoder=clip.cond_stage_model.qwen3,
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tokenizer=clip.tokenizer.qwen3,
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)
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self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
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self.forge_objects_original = self.forge_objects.shallow_copy()
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self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
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@torch.inference_mode()
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def get_learned_conditioning(self, prompt: "SdConditioning"):
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memory_management.load_model_gpu(self.forge_objects.clip.patcher)
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if not prompt.is_negative_prompt:
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_references = [*self.ref_latents]
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if self.ini_latent is not None:
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_references.insert(0, self.ini_latent)
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self.ini_latent = None
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dynamic_args.ref_latents = _references.copy()
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return self.text_processing_engine_gemma(prompt)
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@torch.inference_mode()
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def get_prompt_lengths_on_ui(self, prompt):
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token_count = len(self.text_processing_engine_gemma.tokenize([prompt])[0])
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return token_count, max(999, token_count)
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@torch.inference_mode()
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def encode_first_stage(self, x):
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sample = self.forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5)
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sample = self.forge_objects.vae.first_stage_model.process_in(sample)
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if opts.klein_no_reference:
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return sample.to(x)
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if dynamic_args.is_referencing:
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self.ref_latents.append(sample.cpu())
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else:
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self.ini_latent = sample.cpu()
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return sample.to(x)
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@torch.inference_mode()
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def decode_first_stage(self, x):
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sample = self.forge_objects.vae.first_stage_model.process_out(x)
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sample = self.forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0
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return sample.to(x)
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