mirror of
https://github.com/lllyasviel/stable-diffusion-webui-forge.git
synced 2026-01-27 03:19:47 +00:00
73 lines
2.7 KiB
Python
73 lines
2.7 KiB
Python
import torch
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from huggingface_guess import model_list
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from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
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from backend.patcher.clip import CLIP
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from backend.patcher.vae import VAE
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from backend.patcher.unet import UnetPatcher
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from backend.text_processing.t5_engine import T5TextProcessingEngine
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from backend.args import dynamic_args
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from backend.modules.k_prediction import PredictionFlux
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from backend import memory_management
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class Chroma(ForgeDiffusionEngine):
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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(
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model_dict={
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't5xxl': huggingface_components['text_encoder']
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},
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tokenizer_dict={
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't5xxl': huggingface_components['tokenizer']
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}
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)
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vae = VAE(model=huggingface_components['vae'])
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k_predictor = PredictionFlux(
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mu=1.0
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)
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unet = UnetPatcher.from_model(
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model=huggingface_components['transformer'],
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diffusers_scheduler=None,
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k_predictor=k_predictor,
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config=estimated_config
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)
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self.text_processing_engine_t5 = T5TextProcessingEngine(
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text_encoder=clip.cond_stage_model.t5xxl,
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tokenizer=clip.tokenizer.t5xxl,
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emphasis_name=dynamic_args['emphasis_name'],
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min_length=1
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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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def set_clip_skip(self, clip_skip):
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pass
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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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return self.text_processing_engine_t5(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_t5.tokenize([prompt])[0])
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return token_count, max(255, 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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