256 lines
7.1 KiB
Python
256 lines
7.1 KiB
Python
import modules.core as core
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import os
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import torch
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import modules.path
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import modules.virtual_memory as virtual_memory
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from comfy.model_base import SDXL, SDXLRefiner
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from modules.patch import cfg_patched
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from modules.expansion import FooocusExpansion
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xl_base: core.StableDiffusionModel = None
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xl_base_hash = ''
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xl_refiner: core.StableDiffusionModel = None
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xl_refiner_hash = ''
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xl_base_patched: core.StableDiffusionModel = None
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xl_base_patched_hash = ''
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@torch.no_grad()
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@torch.inference_mode()
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def refresh_base_model(name):
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global xl_base, xl_base_hash, xl_base_patched, xl_base_patched_hash
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filename = os.path.abspath(os.path.realpath(os.path.join(modules.path.modelfile_path, name)))
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model_hash = filename
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if xl_base_hash == model_hash:
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return
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if xl_base is not None:
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xl_base.to_meta()
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xl_base = None
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xl_base = core.load_model(filename)
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if not isinstance(xl_base.unet.model, SDXL):
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print('Model not supported. Fooocus only support SDXL model as the base model.')
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xl_base = None
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xl_base_hash = ''
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refresh_base_model(modules.path.default_base_model_name)
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xl_base_hash = model_hash
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xl_base_patched = xl_base
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xl_base_patched_hash = ''
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return
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xl_base_hash = model_hash
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xl_base_patched = xl_base
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xl_base_patched_hash = ''
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print(f'Base model loaded: {model_hash}')
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return
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@torch.no_grad()
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@torch.inference_mode()
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def refresh_refiner_model(name):
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global xl_refiner, xl_refiner_hash
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filename = os.path.abspath(os.path.realpath(os.path.join(modules.path.modelfile_path, name)))
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model_hash = filename
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if xl_refiner_hash == model_hash:
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return
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if name == 'None':
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xl_refiner = None
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xl_refiner_hash = ''
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print(f'Refiner unloaded.')
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return
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if xl_refiner is not None:
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xl_refiner.to_meta()
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xl_refiner = None
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xl_refiner = core.load_model(filename)
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if not isinstance(xl_refiner.unet.model, SDXLRefiner):
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print('Model not supported. Fooocus only support SDXL refiner as the refiner.')
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xl_refiner = None
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xl_refiner_hash = ''
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print(f'Refiner unloaded.')
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return
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xl_refiner_hash = model_hash
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print(f'Refiner model loaded: {model_hash}')
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xl_refiner.vae.first_stage_model.to('meta')
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xl_refiner.vae = None
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return
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@torch.no_grad()
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@torch.inference_mode()
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def refresh_loras(loras):
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global xl_base, xl_base_patched, xl_base_patched_hash
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if xl_base_patched_hash == str(loras):
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return
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model = xl_base
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for name, weight in loras:
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if name == 'None':
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continue
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filename = os.path.join(modules.path.lorafile_path, name)
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model = core.load_lora(model, filename, strength_model=weight, strength_clip=weight)
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xl_base_patched = model
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xl_base_patched_hash = str(loras)
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print(f'LoRAs loaded: {xl_base_patched_hash}')
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return
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@torch.no_grad()
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@torch.inference_mode()
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def clip_encode_single(clip, text, verbose=False):
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cached = clip.fcs_cond_cache.get(text, None)
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if cached is not None:
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if verbose:
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print(f'[CLIP Cached] {text}')
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return cached
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tokens = clip.tokenize(text)
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result = clip.encode_from_tokens(tokens, return_pooled=True)
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clip.fcs_cond_cache[text] = result
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if verbose:
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print(f'[CLIP Encoded] {text}')
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return result
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@torch.no_grad()
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@torch.inference_mode()
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def clip_encode(sd, texts, pool_top_k=1):
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if sd is None:
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return None
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if sd.clip is None:
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return None
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if not isinstance(texts, list):
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return None
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if len(texts) == 0:
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return None
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clip = sd.clip
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cond_list = []
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pooled_acc = 0
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for i, text in enumerate(texts):
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cond, pooled = clip_encode_single(clip, text)
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cond_list.append(cond)
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if i < pool_top_k:
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pooled_acc += pooled
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return [[torch.cat(cond_list, dim=1), {"pooled_output": pooled_acc}]]
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@torch.no_grad()
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@torch.inference_mode()
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def clear_sd_cond_cache(sd):
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if sd is None:
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return None
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if sd.clip is None:
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return None
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sd.clip.fcs_cond_cache = {}
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return
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@torch.no_grad()
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@torch.inference_mode()
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def clear_all_caches():
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clear_sd_cond_cache(xl_base_patched)
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clear_sd_cond_cache(xl_refiner)
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@torch.no_grad()
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@torch.inference_mode()
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def refresh_everything(refiner_model_name, base_model_name, loras):
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refresh_refiner_model(refiner_model_name)
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if xl_refiner is not None:
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virtual_memory.try_move_to_virtual_memory(xl_refiner.unet.model)
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virtual_memory.try_move_to_virtual_memory(xl_refiner.clip.cond_stage_model)
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refresh_base_model(base_model_name)
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virtual_memory.load_from_virtual_memory(xl_base.unet.model)
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refresh_loras(loras)
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clear_all_caches()
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return
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refresh_everything(
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refiner_model_name=modules.path.default_refiner_model_name,
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base_model_name=modules.path.default_base_model_name,
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loras=[(modules.path.default_lora_name, 0.5), ('None', 0.5), ('None', 0.5), ('None', 0.5), ('None', 0.5)]
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)
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expansion = FooocusExpansion()
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@torch.no_grad()
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@torch.inference_mode()
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def patch_all_models():
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assert xl_base is not None
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assert xl_base_patched is not None
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xl_base.unet.model_options['sampler_cfg_function'] = cfg_patched
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xl_base_patched.unet.model_options['sampler_cfg_function'] = cfg_patched
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if xl_refiner is not None:
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xl_refiner.unet.model_options['sampler_cfg_function'] = cfg_patched
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return
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@torch.no_grad()
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@torch.inference_mode()
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def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, latent=None, denoise=1.0, tiled=False):
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patch_all_models()
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if xl_refiner is not None:
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virtual_memory.try_move_to_virtual_memory(xl_refiner.unet.model)
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virtual_memory.load_from_virtual_memory(xl_base.unet.model)
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if latent is None:
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empty_latent = core.generate_empty_latent(width=width, height=height, batch_size=1)
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else:
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empty_latent = latent
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if xl_refiner is not None:
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sampled_latent = core.ksampler_with_refiner(
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model=xl_base_patched.unet,
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positive=positive_cond[0],
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negative=negative_cond[0],
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refiner=xl_refiner.unet,
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refiner_positive=positive_cond[1],
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refiner_negative=negative_cond[1],
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refiner_switch_step=switch,
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latent=empty_latent,
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steps=steps, start_step=0, last_step=steps, disable_noise=False, force_full_denoise=True,
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seed=image_seed,
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denoise=denoise,
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callback_function=callback
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)
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else:
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sampled_latent = core.ksampler(
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model=xl_base_patched.unet,
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positive=positive_cond[0],
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negative=negative_cond[0],
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latent=empty_latent,
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steps=steps, start_step=0, last_step=steps, disable_noise=False, force_full_denoise=True,
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seed=image_seed,
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denoise=denoise,
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callback_function=callback
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)
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decoded_latent = core.decode_vae(vae=xl_base_patched.vae, latent_image=sampled_latent, tiled=tiled)
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images = core.pytorch_to_numpy(decoded_latent)
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return images
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