mirror of
https://github.com/lllyasviel/stable-diffusion-webui-forge.git
synced 2026-07-21 21:01:24 +08:00
63 lines
2.6 KiB
Python
63 lines
2.6 KiB
Python
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.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
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from backend.modules.k_prediction import PredictionDiscreteFlow
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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.qwen3_engine import Qwen3TextProcessingEngine
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class ZImage(ForgeDiffusionEngine):
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matched_guesses = [model_list.ZImage]
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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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self.is_inpaint = False
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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"])
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k_predictor = PredictionDiscreteFlow(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 = Qwen3TextProcessingEngine(
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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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self.use_shift = True
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@torch.inference_mode()
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def get_learned_conditioning(self, prompt: list[str]):
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memory_management.load_model_gpu(self.forge_objects.clip.patcher)
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shift = getattr(prompt, "distilled_cfg_scale", 3.0)
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self.forge_objects.unet.model.predictor.set_parameters(shift=shift)
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memory_management.logger.debug(f"Shift: {shift}")
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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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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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