mirror of
https://github.com/lllyasviel/stable-diffusion-webui-forge.git
synced 2026-07-21 21:01:24 +08:00
78 lines
3.1 KiB
Python
78 lines
3.1 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.anima_engine import AnimaTextProcessingEngine
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class Anima(ForgeDiffusionEngine):
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matched_guesses = [model_list.Anima]
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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_06b": huggingface_components["text_encoder"]}, tokenizer_dict={"qwen3_06b": huggingface_components["tokenizer"], "t5xxl": huggingface_components["tokenizer_2"]})
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vae = VAE(model=huggingface_components["vae"], is_wan=True)
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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_anima = AnimaTextProcessingEngine(
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text_encoder=clip.cond_stage_model.qwen3_06b,
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qwen_tokenizer=clip.tokenizer.qwen3_06b,
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t5_tokenizer=clip.tokenizer.t5xxl,
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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.is_wan = True
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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_anima(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_anima.tokenize([prompt])[0][0])
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return token_count, max(512, token_count)
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@torch.inference_mode()
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def encode_first_stage(self, x: torch.Tensor):
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samples: list[torch.Tensor] = []
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batch: int = x.size(0)
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for b in range(batch):
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y = x[b].unsqueeze(0)
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sample = self.forge_objects.vae.encode(y.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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samples.append(sample)
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return torch.cat(samples).to(x)
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@torch.inference_mode()
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def decode_first_stage(self, x: torch.Tensor):
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samples: list[torch.Tensor] = []
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batch: int = x.size(0)
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for b in range(batch):
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y = x[b].unsqueeze(0)
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sample = self.forge_objects.vae.first_stage_model.process_out(y)
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sample = self.forge_objects.vae.decode(sample).movedim(-1, 2) * 2.0 - 1.0
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samples.append(sample)
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return torch.cat(samples).to(x)
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