502 lines
16 KiB
Python
502 lines
16 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.patch
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import modules.path
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import fcbh.model_management
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import fcbh.latent_formats
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import modules.inpaint_worker
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import fooocus_extras.vae_interpose as vae_interpose
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from fcbh.model_base import SDXL, SDXLRefiner
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from modules.expansion import FooocusExpansion
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from modules.sample_hijack import clip_separate
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from fcbh.k_diffusion.sampling import BrownianTreeNoiseSampler
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xl_base: core.StableDiffusionModel = None
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xl_base_hash = ''
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xl_base_patched: core.StableDiffusionModel = None
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xl_base_patched_hash = ''
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xl_refiner: core.StableDiffusionModel = None
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xl_refiner_hash = ''
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final_expansion = None
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final_unet = None
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final_clip = None
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final_vae = None
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final_refiner_unet = None
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final_refiner_vae = None
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loaded_ControlNets = {}
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@torch.no_grad()
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@torch.inference_mode()
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def refresh_controlnets(model_paths):
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global loaded_ControlNets
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cache = {}
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for p in model_paths:
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if p is not None:
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if p in loaded_ControlNets:
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cache[p] = loaded_ControlNets[p]
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else:
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cache[p] = core.load_controlnet(p)
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loaded_ControlNets = 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 assert_model_integrity():
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error_message = None
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if xl_base is None:
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error_message = 'You have not selected SDXL base model.'
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if xl_base_patched is None:
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error_message = 'You have not selected SDXL base model.'
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if not isinstance(xl_base.unet.model, SDXL):
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error_message = 'You have selected base model other than SDXL. This is not supported yet.'
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if not isinstance(xl_base_patched.unet.model, SDXL):
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error_message = 'You have selected base model other than SDXL. This is not supported yet.'
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if xl_refiner is not None:
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if xl_refiner.unet is None or xl_refiner.unet.model is None:
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error_message = 'You have selected an invalid refiner!'
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# elif not isinstance(xl_refiner.unet.model, SDXL) and not isinstance(xl_refiner.unet.model, SDXLRefiner):
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# error_message = 'SD1.5 or 2.1 as refiner is not supported!'
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if error_message is not None:
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raise NotImplementedError(error_message)
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return True
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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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xl_base = None
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xl_base_hash = ''
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xl_base_patched = None
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xl_base_patched_hash = ''
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xl_base = core.load_model(filename)
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xl_base_hash = model_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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xl_refiner = None
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xl_refiner_hash = ''
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if name == 'None':
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print(f'Refiner unloaded.')
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return
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xl_refiner = core.load_model(filename)
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xl_refiner_hash = model_hash
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print(f'Refiner model loaded: {model_hash}')
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if isinstance(xl_refiner.unet.model, SDXL):
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xl_refiner.clip = None
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xl_refiner.vae = None
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elif isinstance(xl_refiner.unet.model, SDXLRefiner):
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xl_refiner.clip = None
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xl_refiner.vae = None
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else:
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xl_refiner.clip = 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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if os.path.exists(name):
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filename = name
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else:
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filename = os.path.join(modules.path.lorafile_path, name)
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assert os.path.exists(filename), 'Lora file not found!'
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model = core.load_sd_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(texts, pool_top_k=1):
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global final_clip
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if final_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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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(final_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_all_caches():
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xl_base.clip.fcs_cond_cache = {}
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xl_base_patched.clip.fcs_cond_cache = {}
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@torch.no_grad()
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@torch.inference_mode()
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def prepare_text_encoder(async_call=True):
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if async_call:
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# TODO: make sure that this is always called in an async way so that users cannot feel it.
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pass
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assert_model_integrity()
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fcbh.model_management.load_models_gpu([final_clip.patcher, final_expansion.patcher])
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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_everything(refiner_model_name, base_model_name, loras):
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global final_unet, final_clip, final_vae, final_refiner_unet, final_refiner_vae, final_expansion
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final_unet = None
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final_clip = None
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final_vae = None
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final_refiner_unet = None
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final_refiner_vae = None
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refresh_refiner_model(refiner_model_name)
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refresh_base_model(base_model_name)
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refresh_loras(loras)
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assert_model_integrity()
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final_unet = xl_base_patched.unet
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final_clip = xl_base_patched.clip
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final_vae = xl_base_patched.vae
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final_unet.model.diffusion_model.in_inpaint = False
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if xl_refiner is not None:
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final_refiner_unet = xl_refiner.unet
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final_refiner_vae = xl_refiner.vae
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if final_refiner_unet is not None:
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final_refiner_unet.model.diffusion_model.in_inpaint = False
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if final_expansion is None:
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final_expansion = FooocusExpansion()
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prepare_text_encoder(async_call=True)
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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=[
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(modules.path.default_lora_name, modules.path.default_lora_weight),
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('None', modules.path.default_lora_weight),
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('None', modules.path.default_lora_weight),
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('None', modules.path.default_lora_weight),
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('None', modules.path.default_lora_weight)
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]
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)
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@torch.no_grad()
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@torch.inference_mode()
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def vae_parse(latent):
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if final_refiner_vae is None:
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return latent
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result = vae_interpose.parse(latent["samples"])
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return {'samples': result}
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@torch.no_grad()
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@torch.inference_mode()
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def calculate_sigmas_all(sampler, model, scheduler, steps):
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from fcbh.samplers import calculate_sigmas_scheduler
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discard_penultimate_sigma = False
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if sampler in ['dpm_2', 'dpm_2_ancestral']:
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steps += 1
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discard_penultimate_sigma = True
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sigmas = calculate_sigmas_scheduler(model, scheduler, steps)
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if discard_penultimate_sigma:
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sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
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return sigmas
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@torch.no_grad()
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@torch.inference_mode()
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def calculate_sigmas(sampler, model, scheduler, steps, denoise):
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if denoise is None or denoise > 0.9999:
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sigmas = calculate_sigmas_all(sampler, model, scheduler, steps)
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else:
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new_steps = int(steps / denoise)
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sigmas = calculate_sigmas_all(sampler, model, scheduler, new_steps)
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sigmas = sigmas[-(steps + 1):]
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return sigmas
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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, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0, refiner_swap_method='joint'):
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global final_unet, final_refiner_unet, final_vae, final_refiner_vae
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assert refiner_swap_method in ['joint', 'separate', 'vae', 'upscale']
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refiner_use_different_vae = final_refiner_vae is not None and final_refiner_unet is not None
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if refiner_swap_method == 'upscale':
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if not refiner_use_different_vae:
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refiner_swap_method = 'joint'
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else:
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if refiner_use_different_vae:
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if denoise > 0.95:
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refiner_swap_method = 'vae'
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else:
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# VAE swap only support full denoise
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# Disable refiner to avoid SD15 in joint/separate swap
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final_refiner_unet = None
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final_refiner_vae = None
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print(f'[Sampler] refiner_swap_method = {refiner_swap_method}')
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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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minmax_sigmas = calculate_sigmas(sampler=sampler_name, scheduler=scheduler_name, model=final_unet.model, steps=steps, denoise=denoise)
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sigma_min, sigma_max = minmax_sigmas[minmax_sigmas > 0].min(), minmax_sigmas.max()
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sigma_min = float(sigma_min.cpu().numpy())
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sigma_max = float(sigma_max.cpu().numpy())
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print(f'[Sampler] sigma_min = {sigma_min}, sigma_max = {sigma_max}')
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modules.patch.globalBrownianTreeNoiseSampler = BrownianTreeNoiseSampler(
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empty_latent['samples'].to(fcbh.model_management.get_torch_device()),
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sigma_min, sigma_max, seed=image_seed, cpu=False)
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decoded_latent = None
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if refiner_swap_method == 'joint':
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sampled_latent = core.ksampler(
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model=final_unet,
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refiner=final_refiner_unet,
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positive=positive_cond,
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negative=negative_cond,
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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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cfg=cfg_scale,
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sampler_name=sampler_name,
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scheduler=scheduler_name,
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refiner_switch=switch,
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previewer_start=0,
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previewer_end=steps,
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)
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decoded_latent = core.decode_vae(vae=final_vae, latent_image=sampled_latent, tiled=tiled)
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if refiner_swap_method == 'upscale':
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sampled_latent = core.ksampler(
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model=final_refiner_unet,
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positive=clip_separate(positive_cond, target_model=final_refiner_unet.model, target_clip=final_clip),
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negative=clip_separate(negative_cond, target_model=final_refiner_unet.model, target_clip=final_clip),
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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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cfg=cfg_scale,
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sampler_name=sampler_name,
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scheduler=scheduler_name,
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previewer_start=0,
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previewer_end=steps,
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)
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decoded_latent = core.decode_vae(vae=final_refiner_vae, latent_image=sampled_latent, tiled=tiled)
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if refiner_swap_method == 'separate':
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sampled_latent = core.ksampler(
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model=final_unet,
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positive=positive_cond,
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negative=negative_cond,
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latent=empty_latent,
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steps=steps, start_step=0, last_step=switch, disable_noise=False, force_full_denoise=False,
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seed=image_seed,
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denoise=denoise,
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callback_function=callback,
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cfg=cfg_scale,
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sampler_name=sampler_name,
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scheduler=scheduler_name,
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previewer_start=0,
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previewer_end=steps,
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)
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print('Refiner swapped by changing ksampler. Noise preserved.')
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target_model = final_refiner_unet
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if target_model is None:
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target_model = final_unet
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print('Use base model to refine itself - this may because of developer mode.')
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sampled_latent = core.ksampler(
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model=target_model,
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positive=clip_separate(positive_cond, target_model=target_model.model, target_clip=final_clip),
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negative=clip_separate(negative_cond, target_model=target_model.model, target_clip=final_clip),
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latent=sampled_latent,
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steps=steps, start_step=switch, last_step=steps, disable_noise=True, 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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cfg=cfg_scale,
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sampler_name=sampler_name,
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scheduler=scheduler_name,
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previewer_start=switch,
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previewer_end=steps,
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)
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target_model = final_refiner_vae
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if target_model is None:
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target_model = final_vae
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decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
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if refiner_swap_method == 'vae':
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modules.patch.eps_record = 'vae'
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if modules.inpaint_worker.current_task is not None:
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modules.inpaint_worker.current_task.unswap()
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sampled_latent = core.ksampler(
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model=final_unet,
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positive=positive_cond,
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negative=negative_cond,
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latent=empty_latent,
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steps=steps, start_step=0, last_step=switch, 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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cfg=cfg_scale,
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sampler_name=sampler_name,
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scheduler=scheduler_name,
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previewer_start=0,
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previewer_end=steps
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)
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print('Fooocus VAE-based swap.')
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target_model = final_refiner_unet
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if target_model is None:
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target_model = final_unet
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print('Use base model to refine itself - this may because of developer mode.')
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sampled_latent = vae_parse(sampled_latent)
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k_sigmas = 1.4
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sigmas = calculate_sigmas(sampler=sampler_name,
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scheduler=scheduler_name,
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model=target_model.model,
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steps=steps,
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denoise=denoise)[switch:] * k_sigmas
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len_sigmas = len(sigmas) - 1
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noise_mean = torch.mean(modules.patch.eps_record, dim=1, keepdim=True)
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if modules.inpaint_worker.current_task is not None:
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modules.inpaint_worker.current_task.swap()
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sampled_latent = core.ksampler(
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model=target_model,
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positive=clip_separate(positive_cond, target_model=target_model.model, target_clip=final_clip),
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negative=clip_separate(negative_cond, target_model=target_model.model, target_clip=final_clip),
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latent=sampled_latent,
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steps=len_sigmas, start_step=0, last_step=len_sigmas, disable_noise=False, force_full_denoise=True,
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seed=image_seed+1,
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denoise=denoise,
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callback_function=callback,
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cfg=cfg_scale,
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sampler_name=sampler_name,
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scheduler=scheduler_name,
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previewer_start=switch,
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previewer_end=steps,
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sigmas=sigmas,
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noise_mean=noise_mean
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)
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target_model = final_refiner_vae
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if target_model is None:
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target_model = final_vae
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decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
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|
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images = core.pytorch_to_numpy(decoded_latent)
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modules.patch.eps_record = None
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return images
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