mirror of
https://github.com/vladmandic/sdnext.git
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fe99d3fe5d
Add support for FLUX.2 Klein distilled models (4B and 9B variants): - Add pipeline loader for Flux2KleinPipeline - Add model detection for 'flux.2' + 'klein' patterns - Add pipeline mapping in shared_items - Add shared Qwen3ForCausalLM text encoder handling: - 4B variants use Z-Image-Turbo's Qwen3-8B - 9B variants use FLUX.2-klein-9B's Qwen3-14B - Add reference entries for distilled (4B, 9B) and base models - Update diffusers commit for Flux2KleinPipeline support
291 lines
12 KiB
Python
291 lines
12 KiB
Python
import os
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import time
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import torch
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import diffusers
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from modules import shared, shared_items, devices, errors, model_tools
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debug_load = os.environ.get('SD_LOAD_DEBUG', None)
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def guess_by_size(fn, current_guess):
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new_guess = None
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if os.path.isfile(fn) and fn.endswith('.safetensors'):
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size = round(os.path.getsize(fn) / 1024 / 1024)
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if (size > 0 and size < 128):
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shared.log.warning(f'Model size smaller than expected: file="{fn}" size={size} MB')
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elif (size >= 316 and size <= 324) or (size >= 156 and size <= 164): # 320 or 160
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shared.log.warning(f'Model detected as VAE model, but attempting to load as model: file="{fn}" size={size} MB')
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new_guess = 'VAE'
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elif (size >= 2002 and size <= 2038): # 2032
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new_guess = 'Stable Diffusion 1.5'
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elif (size >= 3138 and size <= 3142): #3140
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new_guess = 'Stable Diffusion XL'
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elif (size >= 3361 and size <= 3369): # 3368
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new_guess = 'Stable Diffusion Upscale'
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elif (size >= 4891 and size <= 4899): # 4897
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new_guess = 'Stable Diffusion XL Inpaint'
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elif (size >= 4970 and size <= 4976): # 4973
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new_guess = 'Stable Diffusion 2' # SD v2 but could be eps or v-prediction
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elif (size >= 5791 and size <= 5799): # 5795
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new_guess = 'Stable Diffusion XL Refiner'
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elif (size > 5692 and size < 5698) or (size > 4134 and size < 4138) or (size > 10362 and size < 10366) or (size > 15028 and size < 15228):
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new_guess = 'Stable Diffusion 3'
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elif (size >= 6420 and size <= 7220): # 6420, IustriousRedux is 6541, monkrenRealisticINT_v10 is 7217
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new_guess = 'Stable Diffusion XL'
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elif (size >= 9791 and size <= 9799): # 9794
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new_guess = 'Stable Diffusion XL Instruct'
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elif (size >= 18414 and size <= 18420): # sd35-large aio
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new_guess = 'Stable Diffusion 3'
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elif (size >= 20000 and size <= 40000):
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new_guess = 'FLUX'
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if debug_load:
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shared.log.trace(f'Autodetect: method=size file="{fn}" size={size} previous="{current_guess}" current="{new_guess}"')
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return new_guess or current_guess
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def guess_by_name(fn, current_guess):
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new_guess = None
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if 'instaflow' in fn.lower():
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new_guess = 'InstaFlow'
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elif 'segmoe' in fn.lower():
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new_guess = 'SegMoE'
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elif 'hunyuandit' in fn.lower():
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new_guess = 'HunyuanDiT'
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elif 'hdm-xut' in fn.lower():
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new_guess = 'hdm'
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elif 'pixart-xl' in fn.lower():
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new_guess = 'PixArt Alpha'
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elif 'stable-diffusion-3' in fn.lower():
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new_guess = 'Stable Diffusion 3'
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elif 'stable-cascade' in fn.lower() or 'stablecascade' in fn.lower() or 'wuerstchen3' in fn.lower() or ('sotediffusion' in fn.lower() and "v2" in fn.lower()):
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if devices.dtype == torch.float16:
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shared.log.warning('Stable Cascade does not support Float16')
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new_guess = 'Stable Cascade'
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elif 'pixart-sigma' in fn.lower():
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new_guess = 'PixArt Sigma'
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elif 'sana' in fn.lower():
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new_guess = 'Sana'
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elif 'lumina-next' in fn.lower():
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new_guess = 'Lumina-Next'
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elif 'lumina-image-2' in fn.lower():
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new_guess = 'Lumina 2'
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elif 'kolors' in fn.lower():
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new_guess = 'Kolors'
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elif 'auraflow' in fn.lower() or 'pony-v7' in fn.lower():
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new_guess = 'AuraFlow'
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elif 'cogview3' in fn.lower():
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new_guess = 'CogView 3'
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elif 'cogview4' in fn.lower():
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new_guess = 'CogView 4'
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elif 'meissonic' in fn.lower():
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new_guess = 'Meissonic'
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elif 'monetico' in fn.lower():
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new_guess = 'Monetico'
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elif 'omnigen2' in fn.lower():
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new_guess = 'OmniGen2'
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elif 'omnigen' in fn.lower():
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new_guess = 'OmniGen'
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elif 'sd3' in fn.lower():
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new_guess = 'Stable Diffusion 3'
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elif 'hidream' in fn.lower():
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new_guess = 'HiDream'
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elif 'chroma' in fn.lower() and 'xl' not in fn.lower():
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new_guess = 'Chroma'
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elif 'flux.2' in fn.lower() and 'klein' in fn.lower():
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new_guess = 'FLUX2 Klein'
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elif 'flux.2' in fn.lower():
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new_guess = 'FLUX2'
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elif 'flux' in fn.lower() or 'flex.1' in fn.lower():
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size = round(os.path.getsize(fn) / 1024 / 1024) if os.path.isfile(fn) else 0
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if size > 11000 and size < 16000:
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shared.log.warning(f'Model detected as FLUX UNET model, but attempting to load a base model: file="{fn}" size={size} MB')
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new_guess = 'FLUX'
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elif 'flex.2' in fn.lower():
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new_guess = 'FLEX'
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elif 'cosmos-predict2' in fn.lower():
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new_guess = 'Cosmos'
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elif 'f-lite' in fn.lower():
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new_guess = 'FLite'
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elif 'wan' in fn.lower():
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new_guess = 'WanAI'
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if 'chronoedit' in fn.lower():
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new_guess = 'ChronoEdit'
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elif 'bria' in fn.lower():
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new_guess = 'Bria'
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elif 'qwen' in fn.lower():
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new_guess = 'Qwen'
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elif 'nextstep' in fn.lower():
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new_guess = 'NextStep'
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elif 'kandinsky-2-1' in fn.lower():
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new_guess = 'Kandinsky 2.1'
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elif 'kandinsky-2-2' in fn.lower():
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new_guess = 'Kandinsky 2.2'
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elif 'kandinsky-3' in fn.lower():
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new_guess = 'Kandinsky 3.0'
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elif 'kandinsky-5.0' in fn.lower():
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new_guess = 'Kandinsky 5.0'
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elif 'hunyuanimage3' in fn.lower() or 'hunyuanimage-3' in fn.lower():
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new_guess = 'HunyuanImage3'
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elif 'hunyuanimage' in fn.lower():
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new_guess = 'HunyuanImage'
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elif 'x-omni' in fn.lower():
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new_guess = 'X-Omni'
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elif 'sdxl-turbo' in fn.lower() or 'stable-diffusion-xl' in fn.lower():
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new_guess = 'Stable Diffusion XL'
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elif 'stable-video-diffusion' in fn.lower():
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new_guess = 'StableVideoDiffusion'
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elif 'prx-' in fn.lower():
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new_guess = 'PRX'
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elif 'gemini-' in fn.lower() and 'image' in fn.lower():
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new_guess = 'NanoBanana'
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elif 'z-image' in fn.lower() or 'z_image' in fn.lower():
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new_guess = 'Z-Image'
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elif 'longcat-image' in fn.lower():
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new_guess = 'LongCat'
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elif 'ovis-image' in fn.lower():
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new_guess = 'Ovis-Image'
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elif 'glm-image' in fn.lower():
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new_guess = 'GLM-Image'
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if debug_load:
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shared.log.trace(f'Autodetect: method=name file="{fn}" previous="{current_guess}" current="{new_guess}"')
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return new_guess or current_guess
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def guess_by_diffusers(fn, current_guess):
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exclude_by_name = ['ostris/Flex.2-preview'] # pipeline may be misleading
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if not os.path.isdir(fn):
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return current_guess, None
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index = os.path.join(fn, 'model_index.json')
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if os.path.exists(index) and os.path.isfile(index):
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index = shared.readfile(index, silent=True, as_type="dict")
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name = index.get('_name_or_path', None)
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if name is not None and name in exclude_by_name:
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return current_guess, None
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cls = index.get('_class_name', None)
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if cls is not None:
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pipeline = getattr(diffusers, cls, None)
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if pipeline is None:
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pipeline = cls
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if callable(pipeline):
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is_quant = False
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for folder in os.listdir(fn):
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folder = os.path.join(fn, folder)
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if is_quant:
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break
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if folder.endswith('quantization_config.json'):
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is_quant = True
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break
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if folder.endswith('config.json'):
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quantization_config = shared.readfile(folder, silent=True, as_type="dict").get("quantization_config", None)
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if quantization_config is not None:
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is_quant = True
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break
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if os.path.isdir(folder):
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for f in os.listdir(folder):
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f = os.path.join(folder, f)
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if f.endswith('quantization_config.json'):
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is_quant = True
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break
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if f.endswith('config.json'):
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quantization_config = shared.readfile(f, silent=True, as_type="dict").get("quantization_config", None)
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if quantization_config is not None:
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is_quant = True
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break
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pipelines = shared_items.get_pipelines()
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for k, v in pipelines.items():
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if v is not None and v.__name__ == pipeline.__name__:
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if is_quant:
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k = f'{k} SDNQ'
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if debug_load:
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shared.log.trace(f'Autodetect: method=diffusers file="{fn}" previous="{current_guess}" current="{k}"')
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return k, v
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return current_guess, None
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def guess_variant(fn, current_guess):
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new_guess = None
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if 'inpaint' in fn.lower():
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if current_guess == 'Stable Diffusion':
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new_guess = 'Stable Diffusion Inpaint'
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elif current_guess == 'Stable Diffusion XL':
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new_guess = 'Stable Diffusion XL Inpaint'
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elif 'instruct' in fn.lower():
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if current_guess == 'Stable Diffusion':
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new_guess = 'Stable Diffusion Instruct'
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elif current_guess == 'Stable Diffusion XL':
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new_guess = 'Stable Diffusion XL Instruct'
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if debug_load:
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shared.log.trace(f'Autodetect: method=variant file="{fn}" previous="{current_guess}" current="{new_guess}"')
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return new_guess or current_guess
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def detect_pipeline(f: str, op: str = 'model'):
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guess = shared.opts.diffusers_pipeline
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pipeline = None
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if guess == 'Autodetect':
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try:
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guess = 'Stable Diffusion XL' if ('XL' in f.upper() or 'SDNQ' in f.upper()) else 'Stable Diffusion' # set default guess
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guess = guess_by_size(f, guess)
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guess = guess_by_name(f, guess)
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guess, pipeline = guess_by_diffusers(f, guess)
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guess = guess_variant(f, guess)
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pipeline = shared_items.get_pipelines().get(guess, None) if pipeline is None else pipeline
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shared.log.info(f'Autodetect {op}: detect="{guess}" class={getattr(pipeline, "__name__", None)} file="{f}"')
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if debug_load is not None:
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t0 = time.time()
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keys = model_tools.get_safetensor_keys(f)
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if keys is not None and len(keys) > 0:
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modules = model_tools.list_to_dict(keys)
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modules = model_tools.remove_entries_after_depth(modules, 3)
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lst = model_tools.list_compact(keys)
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t1 = time.time()
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shared.log.debug(f'Autodetect: modules={modules} list={lst} time={t1-t0:.2f}')
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except Exception as e:
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shared.log.error(f'Autodetect {op}: file="{f}" {e}')
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if debug_load:
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errors.display(e, f'Load {op}: {f}')
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return None, None
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else:
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try:
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pipeline = shared_items.get_pipelines().get(guess, None) if pipeline is None else pipeline
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shared.log.info(f'Load {op}: detect="{guess}" class={getattr(pipeline, "__name__", None)} file="{f}"')
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except Exception as e:
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shared.log.error(f'Load {op}: detect="{guess}" file="{f}" {e}')
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if pipeline is None:
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pipeline = diffusers.DiffusionPipeline
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return pipeline, guess
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def get_load_config(model_file, model_type, config_type='yaml'):
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model_type = model_type.removesuffix(' SDNQ')
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if config_type == 'yaml':
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yaml = os.path.splitext(model_file)[0] + '.yaml'
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if os.path.exists(yaml):
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return yaml
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if model_type == 'Stable Diffusion':
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return 'configs/v1-inference.yaml'
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if model_type == 'Stable Diffusion XL':
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return 'configs/sd_xl_base.yaml'
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if model_type == 'Stable Diffusion XL Refiner':
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return 'configs/sd_xl_refiner.yaml'
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if model_type == 'Stable Diffusion 2':
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return None # dont know if its eps or v so let diffusers sort it out
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# return 'configs/v2-inference-512-base.yaml'
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# return 'configs/v2-inference-768-v.yaml'
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elif config_type == 'json':
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if not shared.opts.diffuser_cache_config:
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return None
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if model_type == 'Stable Diffusion':
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return 'configs/sd15'
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if model_type == 'Stable Diffusion XL':
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return 'configs/sdxl'
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if model_type == 'Stable Diffusion XL Refiner':
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return 'configs/sdxl-refiner'
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if model_type == 'Stable Diffusion 3':
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return 'configs/sd3'
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if model_type == 'FLUX':
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return 'configs/flux'
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return None
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