mirror of https://github.com/vladmandic/automatic
Vladimir Mandic
GitHub
commit
50e4c208ac
26
CHANGELOG.md
26
CHANGELOG.md
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@ -1,5 +1,25 @@
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# Change Log for SD.Next
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## Update for 2025-10-18
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- **Models**
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[Kandinsky 5 Lite](https://huggingface.co/ai-forever/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers') in *SFT, CFG-distilled and Steps-distilled* variants
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first model in Kandinsky5 series is T2V model optimized for 5sec videos and uses Qwen2.5 text encoder
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- **Fixes**
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- ROCm-on-Windows additional checks
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- SDNQ-SVD fallback on incompatible layers
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- Huggingface model download
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- Video implement dynamic and manual sampler shift
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- Fix interrupt batch processing
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- Delay import of control processors until used
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- Fix tiny VAE with batched results
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- Fix CFG scale not added to metadata and set valid range to >=1.0
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- **Other**
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- Optimized Video tab layout
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- Video enable VAE slicing and framewise decoding when possible
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- Detect and log `flash-attn` and `sageattention` if installed
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- Remove unused UI settings
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## Update for 2025-10-17
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### Highlights for 2025-10-17
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@ -15,7 +35,11 @@ Highlight are:
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- **Quantization**:
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new **SVD**-style quantization using SDNQ offers almost zero-loss even with **4bit** quantization
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and now you can also test your favorite quantization on-the-fly and then save/load model for future use
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- Other: support for **Huggingface** mirrors, changes to installer to prevent unwanted `torch-cpu` operations, improved previews, etc.
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- Other: support for **Huggingface** mirrors, changes to installer to prevent unwanted `torch-cpu` operations, improved VAE previews, etc.
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[ReadMe](https://github.com/vladmandic/automatic/blob/master/README.md) | [ChangeLog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) | [Docs](https://vladmandic.github.io/sdnext-docs/) | [WiKi](https://github.com/vladmandic/automatic/wiki) | [Discord](https://discord.com/invite/sd-next-federal-batch-inspectors-1101998836328697867) | [Sponsor](https://github.com/sponsors/vladmandic)
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### Details for 2025-10-17
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3
TODO.md
3
TODO.md
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@ -4,6 +4,7 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
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## Future Candidates
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- Transformers unified cache handler
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- Remote TE
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- [Canvas](https://konvajs.org/)
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- Refactor: [Modular pipelines and guiders](https://github.com/huggingface/diffusers/issues/11915)
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@ -23,10 +24,8 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
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- [SmoothCache](https://github.com/huggingface/diffusers/issues/11135)
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- [MagCache](https://github.com/lllyasviel/FramePack/pull/673/files)
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- [Dream0 guidance](https://huggingface.co/ByteDance/DreamO)
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- [SUPIR upscaler](https://github.com/Fanghua-Yu/SUPIR)
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- [ByteDance OneReward](https://github.com/bytedance/OneReward)
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- [ByteDance USO](https://github.com/bytedance/USO)
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- Remove: `Agent Scheduler`
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- Remove: `CodeFormer`
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- Remove: `GFPGAN`
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- ModernUI: Lite vs Expert mode
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@ -1 +1 @@
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Subproject commit db79b18f6f3f5a247e710b507d10cb39b01cc371
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Subproject commit 8f6427aa037b654ae664a0197c794e48fdbbc648
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38
installer.py
38
installer.py
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@ -608,7 +608,7 @@ def check_diffusers():
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if args.skip_git:
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install('diffusers')
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return
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sha = 'af769881d37fe916afef2c47279f66c79f5f2714' # diffusers commit hash
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sha = '23ebbb4bc81a17ebea17cb7cb94f301199e49a7f' # diffusers commit hash
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# if args.use_rocm or args.use_zluda or args.use_directml:
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# sha = '043ab2520f6a19fce78e6e060a68dbc947edb9f9' # lock diffusers versions for now
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pkg = pkg_resources.working_set.by_key.get('diffusers', None)
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@ -682,11 +682,8 @@ def install_rocm_zluda():
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amd_gpus = []
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try:
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if sys.platform == "win32" and not rocm.is_installed:
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amd_gpus = rocm.driver_get_agents()
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else:
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amd_gpus = rocm.get_agents()
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log.info('ROCm: AMD toolkit detected')
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amd_gpus = rocm.get_agents()
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log.info('ROCm: AMD toolkit detected')
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except Exception as e:
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log.warning(f'ROCm agent enumerator failed: {e}')
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@ -712,10 +709,13 @@ def install_rocm_zluda():
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if device_id < len(amd_gpus):
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device = amd_gpus[device_id]
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if sys.platform == "win32" and args.use_rocm and not rocm.is_installed:
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if sys.platform == "win32" and args.use_rocm and not rocm.is_installed and device is not None:
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check_python(supported_minors=[11, 12, 13], reason='ROCm backend requires a Python version between 3.11 and 3.13')
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install(f"rocm rocm-sdk-core --index-url https://rocm.nightlies.amd.com/v2-staging/{device.therock}")
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rocm.refresh()
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if device.therock is None:
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log.warning('No supported ROCm agent was found. Skipping ROCm package installation.')
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else:
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install(f"rocm rocm-sdk-core --index-url https://rocm.nightlies.amd.com/v2-staging/{device.therock}")
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rocm.refresh()
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msg = f'ROCm: version={rocm.version}'
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if device is not None:
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@ -724,7 +724,9 @@ def install_rocm_zluda():
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if sys.platform == "win32":
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if args.use_rocm: # TODO install: switch to pytorch source when it becomes available
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if device is not None and isinstance(rocm.environment, rocm.PythonPackageEnvironment): # TheRock
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if device is None:
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log.warning('No ROCm agent was found. Please make sure that graphics driver is installed and up to date.')
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if isinstance(rocm.environment, rocm.PythonPackageEnvironment):
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check_python(supported_minors=[11, 12, 13], reason='ROCm backend requires a Python version between 3.11 and 3.13')
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torch_command = os.environ.get('TORCH_COMMAND', f'torch torchvision --index-url https://rocm.nightlies.amd.com/v2-staging/{device.therock}')
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else:
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@ -885,7 +887,6 @@ def check_torch():
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if args.profile:
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pr = cProfile.Profile()
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pr.enable()
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from modules import rocm
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allow_cuda = not (args.use_rocm or args.use_directml or args.use_ipex or args.use_openvino)
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allow_rocm = not (args.use_cuda or args.use_directml or args.use_ipex or args.use_openvino)
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allow_ipex = not (args.use_cuda or args.use_rocm or args.use_directml or args.use_openvino)
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@ -902,11 +903,17 @@ def check_torch():
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log.error('DirectML is only supported on Windows')
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if torch_command != '':
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pass
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is_cuda_available = False
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is_ipex_available = False
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is_rocm_available = False
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else:
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is_cuda_available = allow_cuda and (args.use_cuda or shutil.which('nvidia-smi') is not None or args.use_xformers or os.path.exists(os.path.join(os.environ.get('SystemRoot') or r'C:\Windows', 'System32', 'nvidia-smi.exe')))
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is_rocm_available = allow_rocm and (args.use_rocm or args.use_zluda or rocm.is_installed)
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is_cuda_available = allow_cuda and (args.use_cuda or shutil.which('nvidia-smi') is not None or os.path.exists(os.path.join(os.environ.get('SystemRoot') or r'C:\Windows', 'System32', 'nvidia-smi.exe')))
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is_ipex_available = allow_ipex and (args.use_ipex or shutil.which('sycl-ls') is not None or shutil.which('sycl-ls.exe') is not None or os.environ.get('ONEAPI_ROOT') is not None or os.path.exists('/opt/intel/oneapi') or os.path.exists("C:/Program Files (x86)/Intel/oneAPI") or os.path.exists("C:/oneAPI") or os.path.exists("C:/Program Files/Intel/Intel Graphics Software"))
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is_rocm_available = False
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if not is_cuda_available and not is_ipex_available and allow_rocm:
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from modules import rocm
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is_rocm_available = allow_rocm and (args.use_rocm or args.use_zluda or rocm.is_installed) # late eval to avoid unnecessary import
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if is_cuda_available and args.use_cuda: # prioritize cuda
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torch_command = install_cuda()
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@ -935,6 +942,7 @@ def check_torch():
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else:
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log.warning('Torch: CPU-only version installed')
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torch_command = os.environ.get('TORCH_COMMAND', 'torch torchvision')
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if args.version:
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return
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@ -994,7 +1002,7 @@ def check_torch():
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if not args.ignore:
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sys.exit(1)
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if rocm.is_installed:
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if is_rocm_available:
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rocm.postinstall()
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if not args.skip_all:
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install_torch_addons()
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@ -6,27 +6,6 @@ from installer import log
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from modules.errors import display
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from modules import devices, images
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from modules.control.proc.hed import HEDdetector
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from modules.control.proc.canny import CannyDetector
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from modules.control.proc.edge import EdgeDetector
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from modules.control.proc.lineart import LineartDetector
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from modules.control.proc.lineart_anime import LineartAnimeDetector
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from modules.control.proc.pidi import PidiNetDetector
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from modules.control.proc.mediapipe_face import MediapipeFaceDetector
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from modules.control.proc.shuffle import ContentShuffleDetector
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from modules.control.proc.leres import LeresDetector
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from modules.control.proc.midas import MidasDetector
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from modules.control.proc.mlsd import MLSDdetector
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from modules.control.proc.normalbae import NormalBaeDetector
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from modules.control.proc.openpose import OpenposeDetector
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from modules.control.proc.dwpose import DWposeDetector
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from modules.control.proc.segment_anything import SamDetector
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from modules.control.proc.zoe import ZoeDetector
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from modules.control.proc.marigold import MarigoldDetector
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from modules.control.proc.dpt import DPTDetector
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from modules.control.proc.glpn import GLPNDetector
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from modules.control.proc.depth_anything import DepthAnythingDetector
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models = {}
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cache_dir = 'models/control/processors'
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@ -36,36 +15,92 @@ config = {
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# placeholder
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'None': {},
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# pose models
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'OpenPose': {'class': OpenposeDetector, 'checkpoint': True, 'params': {'include_body': True, 'include_hand': False, 'include_face': False}},
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'DWPose': {'class': DWposeDetector, 'checkpoint': False, 'model': 'Tiny', 'params': {'min_confidence': 0.3}},
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'MediaPipe Face': {'class': MediapipeFaceDetector, 'checkpoint': False, 'params': {'max_faces': 1, 'min_confidence': 0.5}},
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'OpenPose': {'class': None, 'checkpoint': True, 'params': {'include_body': True, 'include_hand': False, 'include_face': False}},
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'DWPose': {'class': None, 'checkpoint': False, 'model': 'Tiny', 'params': {'min_confidence': 0.3}},
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'MediaPipe Face': {'class': None, 'checkpoint': False, 'params': {'max_faces': 1, 'min_confidence': 0.5}},
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# outline models
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'Canny': {'class': CannyDetector, 'checkpoint': False, 'params': {'low_threshold': 100, 'high_threshold': 200}},
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'Edge': {'class': EdgeDetector, 'checkpoint': False, 'params': {'pf': True, 'mode': 'edge'}},
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'LineArt Realistic': {'class': LineartDetector, 'checkpoint': True, 'params': {'coarse': False}},
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'LineArt Anime': {'class': LineartAnimeDetector, 'checkpoint': True, 'params': {}},
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'HED': {'class': HEDdetector, 'checkpoint': True, 'params': {'scribble': False, 'safe': False}},
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'PidiNet': {'class': PidiNetDetector, 'checkpoint': True, 'params': {'scribble': False, 'safe': False, 'apply_filter': False}},
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'Canny': {'class': None, 'checkpoint': False, 'params': {'low_threshold': 100, 'high_threshold': 200}},
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'Edge': {'class': None, 'checkpoint': False, 'params': {'pf': True, 'mode': 'edge'}},
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'LineArt Realistic': {'class': None, 'checkpoint': True, 'params': {'coarse': False}},
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'LineArt Anime': {'class': None, 'checkpoint': True, 'params': {}},
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'HED': {'class': None, 'checkpoint': True, 'params': {'scribble': False, 'safe': False}},
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'PidiNet': {'class': None, 'checkpoint': True, 'params': {'scribble': False, 'safe': False, 'apply_filter': False}},
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# depth models
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'Midas Depth Hybrid': {'class': MidasDetector, 'checkpoint': True, 'params': {'bg_th': 0.1, 'depth_and_normal': False}},
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'Leres Depth': {'class': LeresDetector, 'checkpoint': True, 'params': {'boost': False, 'thr_a':0, 'thr_b':0}},
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'Zoe Depth': {'class': ZoeDetector, 'checkpoint': True, 'params': {'gamma_corrected': False}, 'load_config': {'pretrained_model_or_path': 'halffried/gyre_zoedepth', 'filename': 'ZoeD_M12_N.safetensors', 'model_type': "zoedepth"}},
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'Marigold Depth': {'class': MarigoldDetector, 'checkpoint': True, 'params': {'denoising_steps': 10, 'ensemble_size': 10, 'processing_res': 512, 'match_input_res': True, 'color_map': 'None'}, 'load_config': {'pretrained_model_or_path': 'Bingxin/Marigold'}},
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'Normal Bae': {'class': NormalBaeDetector, 'checkpoint': True, 'params': {}},
|
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'Midas Depth Hybrid': {'class': None, 'checkpoint': True, 'params': {'bg_th': 0.1, 'depth_and_normal': False}},
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'Leres Depth': {'class': None, 'checkpoint': True, 'params': {'boost': False, 'thr_a':0, 'thr_b':0}},
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'Zoe Depth': {'class': None, 'checkpoint': True, 'params': {'gamma_corrected': False}, 'load_config': {'pretrained_model_or_path': 'halffried/gyre_zoedepth', 'filename': 'ZoeD_M12_N.safetensors', 'model_type': "zoedepth"}},
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'Marigold Depth': {'class': None, 'checkpoint': True, 'params': {'denoising_steps': 10, 'ensemble_size': 10, 'processing_res': 512, 'match_input_res': True, 'color_map': 'None'}, 'load_config': {'pretrained_model_or_path': 'Bingxin/Marigold'}},
|
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'Normal Bae': {'class': None, 'checkpoint': True, 'params': {}},
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# segmentation models
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'SegmentAnything': {'class': SamDetector, 'checkpoint': True, 'model': 'Base', 'params': {}},
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'SegmentAnything': {'class': None, 'checkpoint': True, 'model': 'Base', 'params': {}},
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# other models
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'MLSD': {'class': MLSDdetector, 'checkpoint': True, 'params': {'thr_v': 0.1, 'thr_d': 0.1}},
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'Shuffle': {'class': ContentShuffleDetector, 'checkpoint': False, 'params': {}},
|
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'DPT Depth Hybrid': {'class': DPTDetector, 'checkpoint': False, 'params': {}},
|
||||
'GLPN Depth': {'class': GLPNDetector, 'checkpoint': False, 'params': {}},
|
||||
'Depth Anything': {'class': DepthAnythingDetector, 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'LiheYoung/depth_anything_vitl14' }, 'params': { 'color_map': 'inferno' }},
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'MLSD': {'class': None, 'checkpoint': True, 'params': {'thr_v': 0.1, 'thr_d': 0.1}},
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'Shuffle': {'class': None, 'checkpoint': False, 'params': {}},
|
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'DPT Depth Hybrid': {'class': None, 'checkpoint': False, 'params': {}},
|
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'GLPN Depth': {'class': None, 'checkpoint': False, 'params': {}},
|
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'Depth Anything': {'class': None, 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'LiheYoung/depth_anything_vitl14' }, 'params': { 'color_map': 'inferno' }},
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# 'Midas Depth Large': {'class': MidasDetector, 'checkpoint': True, 'params': {'bg_th': 0.1, 'depth_and_normal': False}, 'load_config': {'pretrained_model_or_path': 'Intel/dpt-large', 'model_type': "dpt_large", 'filename': ''}},
|
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# 'Zoe Depth Zoe': {'class': ZoeDetector, 'checkpoint': True, 'params': {}},
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# 'Zoe Depth NK': {'class': ZoeDetector, 'checkpoint': True, 'params': {}, 'load_config': {'pretrained_model_or_path': 'halffried/gyre_zoedepth', 'filename': 'ZoeD_M12_NK.safetensors', 'model_type': "zoedepth_nk"}},
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}
|
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|
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|
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def delay_load_config():
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global config # pylint: disable=global-statement
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||||
from modules.control.proc.hed import HEDdetector
|
||||
from modules.control.proc.canny import CannyDetector
|
||||
from modules.control.proc.edge import EdgeDetector
|
||||
from modules.control.proc.lineart import LineartDetector
|
||||
from modules.control.proc.lineart_anime import LineartAnimeDetector
|
||||
from modules.control.proc.pidi import PidiNetDetector
|
||||
from modules.control.proc.mediapipe_face import MediapipeFaceDetector
|
||||
from modules.control.proc.shuffle import ContentShuffleDetector
|
||||
from modules.control.proc.leres import LeresDetector
|
||||
from modules.control.proc.midas import MidasDetector
|
||||
from modules.control.proc.mlsd import MLSDdetector
|
||||
from modules.control.proc.normalbae import NormalBaeDetector
|
||||
from modules.control.proc.openpose import OpenposeDetector
|
||||
from modules.control.proc.dwpose import DWposeDetector
|
||||
from modules.control.proc.segment_anything import SamDetector
|
||||
from modules.control.proc.zoe import ZoeDetector
|
||||
from modules.control.proc.marigold import MarigoldDetector
|
||||
from modules.control.proc.dpt import DPTDetector
|
||||
from modules.control.proc.glpn import GLPNDetector
|
||||
from modules.control.proc.depth_anything import DepthAnythingDetector
|
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config = {
|
||||
# placeholder
|
||||
'None': {},
|
||||
# pose models
|
||||
'OpenPose': {'class': OpenposeDetector, 'checkpoint': True, 'params': {'include_body': True, 'include_hand': False, 'include_face': False}},
|
||||
'DWPose': {'class': DWposeDetector, 'checkpoint': False, 'model': 'Tiny', 'params': {'min_confidence': 0.3}},
|
||||
'MediaPipe Face': {'class': MediapipeFaceDetector, 'checkpoint': False, 'params': {'max_faces': 1, 'min_confidence': 0.5}},
|
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# outline models
|
||||
'Canny': {'class': CannyDetector, 'checkpoint': False, 'params': {'low_threshold': 100, 'high_threshold': 200}},
|
||||
'Edge': {'class': EdgeDetector, 'checkpoint': False, 'params': {'pf': True, 'mode': 'edge'}},
|
||||
'LineArt Realistic': {'class': LineartDetector, 'checkpoint': True, 'params': {'coarse': False}},
|
||||
'LineArt Anime': {'class': LineartAnimeDetector, 'checkpoint': True, 'params': {}},
|
||||
'HED': {'class': HEDdetector, 'checkpoint': True, 'params': {'scribble': False, 'safe': False}},
|
||||
'PidiNet': {'class': PidiNetDetector, 'checkpoint': True, 'params': {'scribble': False, 'safe': False, 'apply_filter': False}},
|
||||
# depth models
|
||||
'Midas Depth Hybrid': {'class': MidasDetector, 'checkpoint': True, 'params': {'bg_th': 0.1, 'depth_and_normal': False}},
|
||||
'Leres Depth': {'class': LeresDetector, 'checkpoint': True, 'params': {'boost': False, 'thr_a':0, 'thr_b':0}},
|
||||
'Zoe Depth': {'class': ZoeDetector, 'checkpoint': True, 'params': {'gamma_corrected': False}, 'load_config': {'pretrained_model_or_path': 'halffried/gyre_zoedepth', 'filename': 'ZoeD_M12_N.safetensors', 'model_type': "zoedepth"}},
|
||||
'Marigold Depth': {'class': MarigoldDetector, 'checkpoint': True, 'params': {'denoising_steps': 10, 'ensemble_size': 10, 'processing_res': 512, 'match_input_res': True, 'color_map': 'None'}, 'load_config': {'pretrained_model_or_path': 'Bingxin/Marigold'}},
|
||||
'Normal Bae': {'class': NormalBaeDetector, 'checkpoint': True, 'params': {}},
|
||||
# segmentation models
|
||||
'SegmentAnything': {'class': SamDetector, 'checkpoint': True, 'model': 'Base', 'params': {}},
|
||||
# other models
|
||||
'MLSD': {'class': MLSDdetector, 'checkpoint': True, 'params': {'thr_v': 0.1, 'thr_d': 0.1}},
|
||||
'Shuffle': {'class': ContentShuffleDetector, 'checkpoint': False, 'params': {}},
|
||||
'DPT Depth Hybrid': {'class': DPTDetector, 'checkpoint': False, 'params': {}},
|
||||
'GLPN Depth': {'class': GLPNDetector, 'checkpoint': False, 'params': {}},
|
||||
'Depth Anything': {'class': DepthAnythingDetector, 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'LiheYoung/depth_anything_vitl14' }, 'params': { 'color_map': 'inferno' }},
|
||||
# 'Midas Depth Large': {'class': MidasDetector, 'checkpoint': True, 'params': {'bg_th': 0.1, 'depth_and_normal': False}, 'load_config': {'pretrained_model_or_path': 'Intel/dpt-large', 'model_type': "dpt_large", 'filename': ''}},
|
||||
# 'Zoe Depth Zoe': {'class': ZoeDetector, 'checkpoint': True, 'params': {}},
|
||||
# 'Zoe Depth NK': {'class': ZoeDetector, 'checkpoint': True, 'params': {}, 'load_config': {'pretrained_model_or_path': 'halffried/gyre_zoedepth', 'filename': 'ZoeD_M12_NK.safetensors', 'model_type': "zoedepth_nk"}},
|
||||
}
|
||||
|
||||
|
||||
def list_models(refresh=False):
|
||||
global models # pylint: disable=global-statement
|
||||
if not refresh and len(models) > 0:
|
||||
|
|
@ -178,6 +213,9 @@ class Processor():
|
|||
log.error(f'Control Processor unknown: id="{processor_id}" available={list(config)}')
|
||||
return f'Processor failed to load: {processor_id}'
|
||||
cls = config[processor_id]['class']
|
||||
if cls is None:
|
||||
delay_load_config()
|
||||
cls = config[processor_id]['class']
|
||||
# log.debug(f'Control Processor loading: id="{processor_id}" class={cls.__name__}')
|
||||
debug(f'Control Processor config={self.load_config}')
|
||||
jobid = state.begin('Load processor')
|
||||
|
|
|
|||
|
|
@ -589,7 +589,7 @@ def control_run(state: str = '', # pylint: disable=keyword-arg-before-vararg
|
|||
if p.scripts is not None:
|
||||
processed = p.scripts.after(p, processed, *p.script_args)
|
||||
output = None
|
||||
if processed is not None:
|
||||
if processed is not None and processed.images is not None:
|
||||
output = processed.images
|
||||
info_txt = [processed.infotext(p, i) for i in range(len(output))]
|
||||
|
||||
|
|
|
|||
|
|
@ -434,7 +434,7 @@ def set_sdpa_params():
|
|||
torch.backends.cuda.enable_math_sdp('Math attention' in opts.sdp_options)
|
||||
if hasattr(torch.backends.cuda, "allow_fp16_bf16_reduction_math_sdp"): # only valid for torch >= 2.5
|
||||
torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True)
|
||||
log.debug(f'Torch attention: type="sdpa" flash={"Flash attention" in opts.sdp_options} memory={"Memory attention" in opts.sdp_options} math={"Math attention" in opts.sdp_options}')
|
||||
log.debug(f'Torch attention: type="sdpa" opts={opts.sdp_options}')
|
||||
except Exception as err:
|
||||
log.warning(f'Torch attention: type="sdpa" {err}')
|
||||
|
||||
|
|
@ -447,7 +447,6 @@ def set_sdpa_params():
|
|||
sdpa_pre_dyanmic_atten = torch.nn.functional.scaled_dot_product_attention
|
||||
from modules.sd_hijack_dynamic_atten import dynamic_scaled_dot_product_attention
|
||||
torch.nn.functional.scaled_dot_product_attention = dynamic_scaled_dot_product_attention
|
||||
log.debug('Torch attention: type="dynamic attention"')
|
||||
except Exception as err:
|
||||
log.error(f'Torch attention: type="dynamic attention" {err}')
|
||||
|
||||
|
|
@ -542,6 +541,17 @@ def set_sdpa_params():
|
|||
log.debug('Torch attention: type="sage attention"')
|
||||
except Exception as err:
|
||||
log.error(f'Torch attention: type="sage attention" {err}')
|
||||
|
||||
from importlib.metadata import version
|
||||
try:
|
||||
flash = version('flash-attn')
|
||||
except Exception:
|
||||
flash = False
|
||||
try:
|
||||
sage = version('sageattention')
|
||||
except Exception:
|
||||
sage = False
|
||||
log.info(f'Torch attention: flashattn={flash} sageattention={sage}')
|
||||
except Exception as e:
|
||||
log.warning(f'Torch SDPA: {e}')
|
||||
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@ def instant_id(p: processing.StableDiffusionProcessing, app, source_images, stre
|
|||
return None
|
||||
|
||||
c = shared.sd_model.__class__.__name__ if shared.sd_loaded else ''
|
||||
if c != 'StableDiffusionXLPipeline' and c != 'StableDiffusionXLInstantIDPipeline':
|
||||
if c not in ['StableDiffusionXLPipeline', 'StableDiffusionXLInstantIDPipeline']:
|
||||
shared.log.warning(f'InstantID invalid base model: current={c} required=StableDiffusionXLPipeline')
|
||||
return None
|
||||
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
import gradio as gr
|
||||
from modules import ui_sections, ui_common, ui_video_vlm
|
||||
from modules.video_models.video_utils import get_codecs
|
||||
from modules import ui_sections, ui_video_vlm
|
||||
from modules.framepack import framepack_load
|
||||
from modules.framepack.framepack_worker import get_latent_paddings
|
||||
from modules.framepack.framepack_wrappers import load_model, unload_model
|
||||
|
|
@ -13,7 +12,7 @@ def change_sections(duration, mp4_fps, mp4_interpolate, latent_ws, variant):
|
|||
return gr.update(value=f'Target video: {num_frames} frames in {num_sections} sections'), gr.update(lines=max(2, 2*num_sections//3))
|
||||
|
||||
|
||||
def create_ui(prompt, negative, styles, _overrides):
|
||||
def create_ui(prompt, negative, styles, _overrides, init_image, last_image, mp4_fps, mp4_interpolate, mp4_codec, mp4_ext, mp4_opt, mp4_video, mp4_frames, mp4_sf):
|
||||
with gr.Row():
|
||||
with gr.Column(variant='compact', elem_id="framepack_settings", elem_classes=['settings-column'], scale=1):
|
||||
with gr.Row():
|
||||
|
|
@ -28,25 +27,12 @@ def create_ui(prompt, negative, styles, _overrides):
|
|||
with gr.Row():
|
||||
section_html = gr.HTML(show_label=False, elem_id="framepack_section_html")
|
||||
with gr.Accordion(label="Inputs", open=False):
|
||||
with gr.Row():
|
||||
input_image = gr.Image(sources='upload', type="numpy", label="FP init image", width=256, height=256, interactive=True, tool="editor", image_mode='RGB', elem_id="framepack_input_image")
|
||||
end_image = gr.Image(sources='upload', type="numpy", label="FP end image", width=256, height=256, interactive=True, tool="editor", image_mode='RGB', elem_id="framepack_end_image")
|
||||
with gr.Row():
|
||||
start_weight = gr.Slider(label="FP init strength", value=1.0, minimum=0.0, maximum=2.0, step=0.05, elem_id="framepack_start_weight")
|
||||
end_weight = gr.Slider(label="FP end strength", value=1.0, minimum=0.0, maximum=2.0, step=0.05, elem_id="framepack_end_weight")
|
||||
vision_weight = gr.Slider(label="FP vision strength", value=1.0, minimum=0.0, maximum=2.0, step=0.05, elem_id="framepack_vision_weight")
|
||||
with gr.Accordion(label="Sections", open=False):
|
||||
section_prompt = gr.Textbox(label="FP section prompts", elem_id="framepack_section_prompt", lines=2, placeholder="Optional one-line prompt suffix per each video section", interactive=True)
|
||||
with gr.Accordion(label="Video", open=False):
|
||||
with gr.Row():
|
||||
mp4_codec = gr.Dropdown(label="FP codec", choices=['none', 'libx264'], value='libx264', type='value')
|
||||
ui_common.create_refresh_button(mp4_codec, get_codecs, elem_id="framepack_mp4_codec_refresh")
|
||||
mp4_ext = gr.Textbox(label="FP format", value='mp4', elem_id="framepack_mp4_ext")
|
||||
mp4_opt = gr.Textbox(label="FP options", value='crf:16', elem_id="framepack_mp4_ext")
|
||||
with gr.Row():
|
||||
mp4_video = gr.Checkbox(label='FP save video', value=True, elem_id="framepack_mp4_video")
|
||||
mp4_frames = gr.Checkbox(label='FP save frames', value=False, elem_id="framepack_mp4_frames")
|
||||
mp4_sf = gr.Checkbox(label='FP save safetensors', value=False, elem_id="framepack_mp4_sf")
|
||||
with gr.Accordion(label="Advanced", open=False):
|
||||
seed = ui_sections.create_seed_inputs('control', reuse_visible=False, subseed_visible=False, accordion=False)[0]
|
||||
latent_ws = gr.Slider(label="FP latent window size", minimum=1, maximum=33, value=9, step=1)
|
||||
|
|
@ -58,7 +44,7 @@ def create_ui(prompt, negative, styles, _overrides):
|
|||
cfg_distilled = gr.Slider(label="FP distilled CFG scale", minimum=1.0, maximum=32.0, value=10.0, step=0.01)
|
||||
cfg_rescale = gr.Slider(label="FP CFG re-scale", minimum=0.0, maximum=1.0, value=0.0, step=0.01)
|
||||
|
||||
vlm_enhance, vlm_model, vlm_system_prompt = ui_video_vlm.create_ui(prompt_element=prompt, image_element=input_image)
|
||||
vlm_enhance, vlm_model, vlm_system_prompt = ui_video_vlm.create_ui(prompt_element=prompt, image_element=init_image)
|
||||
|
||||
with gr.Accordion(label="Model", open=False):
|
||||
with gr.Row():
|
||||
|
|
@ -108,7 +94,7 @@ def create_ui(prompt, negative, styles, _overrides):
|
|||
receipe_reset.click(fn=framepack_load.reset_model, inputs=[], outputs=[receipe])
|
||||
|
||||
framepack_inputs=[
|
||||
input_image, end_image,
|
||||
init_image, last_image,
|
||||
start_weight, end_weight, vision_weight,
|
||||
prompt, system_prompt, optimized_prompt, section_prompt, negative, styles,
|
||||
seed,
|
||||
|
|
|
|||
|
|
@ -37,6 +37,7 @@ def run_ltx(task_id,
|
|||
refine_strength:float,
|
||||
condition_strength: float,
|
||||
condition_image,
|
||||
condition_last,
|
||||
condition_files,
|
||||
condition_video,
|
||||
condition_video_frames:int,
|
||||
|
|
@ -100,11 +101,16 @@ def run_ltx(task_id,
|
|||
)
|
||||
p.ops.append('video')
|
||||
|
||||
condition_images = []
|
||||
if condition_image is not None:
|
||||
condition_images.append(condition_image)
|
||||
if condition_last is not None:
|
||||
condition_images.append(condition_last)
|
||||
conditions = get_conditions(
|
||||
width,
|
||||
height,
|
||||
condition_strength,
|
||||
condition_image,
|
||||
condition_images,
|
||||
condition_files,
|
||||
condition_video,
|
||||
condition_video_frames,
|
||||
|
|
|
|||
|
|
@ -1,8 +1,6 @@
|
|||
import os
|
||||
import gradio as gr
|
||||
from modules import shared, ui_sections, ui_symbols, ui_common
|
||||
from modules.ui_components import ToolButton
|
||||
from modules.video_models.video_utils import get_codecs
|
||||
from modules import shared, ui_sections
|
||||
from modules.video_models.models_def import models
|
||||
from modules.ltx import ltx_process
|
||||
|
||||
|
|
@ -10,7 +8,7 @@ from modules.ltx import ltx_process
|
|||
debug = shared.log.trace if os.environ.get('SD_VIDEO_DEBUG', None) is not None else lambda *args, **kwargs: None
|
||||
|
||||
|
||||
def create_ui(prompt, negative, styles, overrides):
|
||||
def create_ui(prompt, negative, styles, overrides, init_image, init_strength, last_image, mp4_fps, mp4_interpolate, mp4_codec, mp4_ext, mp4_opt, mp4_video, mp4_frames, mp4_sf, width, height, frames, seed):
|
||||
with gr.Row():
|
||||
with gr.Column(variant='compact', elem_id="ltx_settings", elem_classes=['settings-column'], scale=1):
|
||||
with gr.Row():
|
||||
|
|
@ -18,18 +16,8 @@ def create_ui(prompt, negative, styles, overrides):
|
|||
with gr.Row():
|
||||
ltx_models = [m.name for m in models['LTX Video']]
|
||||
model = gr.Dropdown(label='LTX model', choices=ltx_models, value=ltx_models[0])
|
||||
with gr.Accordion(open=True, label="LTX size", elem_id='ltx_generate_accordion'):
|
||||
with gr.Row():
|
||||
width, height = ui_sections.create_resolution_inputs('ltx', default_width=832, default_height=480)
|
||||
with gr.Row():
|
||||
frames = gr.Slider(label='LTX frames', minimum=1, maximum=513, step=1, value=17, elem_id="ltx_frames")
|
||||
seed = gr.Number(label='LTX seed', value=-1, elem_id="ltx_seed", container=True)
|
||||
random_seed = ToolButton(ui_symbols.random, elem_id="ltx_seed_random")
|
||||
with gr.Accordion(open=False, label="Condition", elem_id='ltx_condition_accordion'):
|
||||
condition_strength = gr.Slider(label='LTX condition strength', minimum=0.1, maximum=1.0, step=0.05, value=0.8, elem_id="ltx_condition_image_strength")
|
||||
with gr.Tabs():
|
||||
with gr.Tab('Image', id='ltx_condition_image_tab'):
|
||||
condition_image = gr.Image(sources='upload', type="pil", label="Image", width=256, height=256, interactive=True, tool="editor", image_mode='RGB', elem_id="ltx_condition_image")
|
||||
with gr.Tab('Video', id='ltx_condition_video_tab'):
|
||||
condition_video = gr.Video(label='Video', type='filepath', elem_id="ltx_condition_video", width=256, height=256, source='upload')
|
||||
with gr.Row():
|
||||
|
|
@ -45,19 +33,6 @@ def create_ui(prompt, negative, styles, overrides):
|
|||
with gr.Row():
|
||||
refine_enable = gr.Checkbox(label='LTX enable refine', value=False, elem_id="ltx_refine_enable")
|
||||
refine_strength = gr.Slider(label='LTX refine strength', minimum=0.1, maximum=1.0, step=0.05, value=0.4, elem_id="ltx_refine_strength")
|
||||
with gr.Accordion(label="Video", open=False):
|
||||
with gr.Row():
|
||||
mp4_fps = gr.Slider(label="FPS", minimum=1, maximum=60, value=24, step=1)
|
||||
mp4_interpolate = gr.Slider(label="LTX interpolation", minimum=0, maximum=10, value=0, step=1)
|
||||
with gr.Row():
|
||||
mp4_codec = gr.Dropdown(label="LTX codec", choices=['none', 'libx264'], value='libx264', type='value')
|
||||
ui_common.create_refresh_button(mp4_codec, get_codecs, elem_id="framepack_mp4_codec_refresh")
|
||||
mp4_ext = gr.Textbox(label="LTX format", value='mp4', elem_id="framepack_mp4_ext")
|
||||
mp4_opt = gr.Textbox(label="LTX options", value='crf:16', elem_id="framepack_mp4_ext")
|
||||
with gr.Row():
|
||||
mp4_video = gr.Checkbox(label='LTX save video', value=True, elem_id="framepack_mp4_video")
|
||||
mp4_frames = gr.Checkbox(label='LTX save frames', value=False, elem_id="framepack_mp4_frames")
|
||||
mp4_sf = gr.Checkbox(label='LTX save safetensors', value=False, elem_id="framepack_mp4_sf")
|
||||
with gr.Accordion(open=False, label="Advanced", elem_id='ltx_parameters_accordion'):
|
||||
steps, sampler_index = ui_sections.create_sampler_and_steps_selection(None, "ltx", default_steps=50)
|
||||
with gr.Row():
|
||||
|
|
@ -71,7 +46,6 @@ def create_ui(prompt, negative, styles, overrides):
|
|||
with gr.Row():
|
||||
text = gr.HTML('', elem_id='ltx_generation_info', show_label=False)
|
||||
|
||||
random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
|
||||
task_id = gr.Textbox(visible=False, value='')
|
||||
ui_state = gr.Textbox(visible=False, value='')
|
||||
state_inputs = [task_id, ui_state]
|
||||
|
|
@ -83,7 +57,7 @@ def create_ui(prompt, negative, styles, overrides):
|
|||
steps, sampler_index, seed,
|
||||
upsample_enable, upsample_ratio,
|
||||
refine_enable, refine_strength,
|
||||
condition_strength, condition_image, condition_files, condition_video, condition_video_frames, condition_video_skip,
|
||||
init_strength, init_image, last_image, condition_files, condition_video, condition_video_frames, condition_video_skip,
|
||||
decode_timestep, image_cond_noise_scale,
|
||||
mp4_fps, mp4_interpolate, mp4_codec, mp4_ext, mp4_opt, mp4_video, mp4_frames, mp4_sf,
|
||||
overrides,
|
||||
|
|
|
|||
|
|
@ -54,19 +54,20 @@ def load_upsample(upsample_pipe, upsample_repo_id):
|
|||
return upsample_pipe
|
||||
|
||||
|
||||
def get_conditions(width, height, condition_strength, condition_image, condition_files, condition_video, condition_video_frames, condition_video_skip):
|
||||
def get_conditions(width, height, condition_strength, condition_images, condition_files, condition_video, condition_video_frames, condition_video_skip):
|
||||
from diffusers.pipelines.ltx.pipeline_ltx_condition import LTXVideoCondition
|
||||
conditions = []
|
||||
if condition_image is not None:
|
||||
try:
|
||||
if isinstance(condition_image, str):
|
||||
from modules.api.api import decode_base64_to_image
|
||||
condition_image = decode_base64_to_image(condition_image)
|
||||
condition_image = condition_image.convert('RGB').resize((width, height), resample=Image.Resampling.LANCZOS)
|
||||
conditions.append(LTXVideoCondition(image=condition_image, frame_index=0, strength=condition_strength))
|
||||
shared.log.debug(f'Video condition: image={condition_image.size} strength={condition_strength}')
|
||||
except Exception as e:
|
||||
shared.log.error(f'LTX condition image: {e}')
|
||||
if condition_images is not None:
|
||||
for condition_image in condition_images:
|
||||
try:
|
||||
if isinstance(condition_image, str):
|
||||
from modules.api.api import decode_base64_to_image
|
||||
condition_image = decode_base64_to_image(condition_image)
|
||||
condition_image = condition_image.convert('RGB').resize((width, height), resample=Image.Resampling.LANCZOS)
|
||||
conditions.append(LTXVideoCondition(image=condition_image, frame_index=0, strength=condition_strength))
|
||||
shared.log.debug(f'Video condition: image={condition_image.size} strength={condition_strength}')
|
||||
except Exception as e:
|
||||
shared.log.error(f'LTX condition image: {e}')
|
||||
if condition_files is not None:
|
||||
condition_images = []
|
||||
for fn in condition_files:
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
import os
|
||||
import time
|
||||
import gradio as gr
|
||||
from installer import log, install
|
||||
from modules.shared import opts
|
||||
|
||||
|
|
@ -37,7 +39,7 @@ def hf_init():
|
|||
obfuscated_token = None
|
||||
if len(opts.huggingface_token) > 0 and opts.huggingface_token.startswith('hf_'):
|
||||
obfuscated_token = 'hf_...' + opts.huggingface_token[-4:]
|
||||
log.info(f'Huggingface init: transfer={opts.hf_transfer_mode} parallel={opts.sd_parallel_load} direct={opts.diffusers_to_gpu} token="{obfuscated_token}" cache="{opts.hfcache_dir}"')
|
||||
log.info(f'Huggingface: transfer={opts.hf_transfer_mode} parallel={opts.sd_parallel_load} direct={opts.diffusers_to_gpu} token="{obfuscated_token}" cache="{opts.hfcache_dir}" init')
|
||||
|
||||
|
||||
def hf_check_cache():
|
||||
|
|
@ -48,22 +50,26 @@ def hf_check_cache():
|
|||
if size//1024//1024 > 0:
|
||||
log.warning(f'Cache location changed: previous="{prev_default}" size={size//1024//1024} MB')
|
||||
size, _mtime = stat(opts.hfcache_dir)
|
||||
log.debug(f'Huggingface cache: path="{opts.hfcache_dir}" size={size//1024//1024} MB')
|
||||
log.debug(f'Huggingface: cache="{opts.hfcache_dir}" size={size//1024//1024} MB')
|
||||
|
||||
|
||||
def hf_search(keyword):
|
||||
import huggingface_hub as hf
|
||||
t0 = time.time()
|
||||
hf_api = hf.HfApi()
|
||||
models = hf_api.list_models(model_name=keyword, full=True, library="diffusers", limit=50, sort="downloads", direction=-1)
|
||||
data = []
|
||||
for model in models:
|
||||
tags = [t for t in model.tags if not t.startswith('diffusers') and not t.startswith('license') and not t.startswith('arxiv') and len(t) > 2]
|
||||
data.append([model.id, model.pipeline_tag, tags, model.downloads, model.lastModified, f'https://huggingface.co/{model.id}'])
|
||||
log.debug(f'Huggingface: search="{keyword}" results={len(data)} time={time.time()-t0:.2f}')
|
||||
return data
|
||||
|
||||
|
||||
def hf_select(evt, data):
|
||||
return data[evt.index[0]][0]
|
||||
def hf_select(evt: gr.SelectData, df):
|
||||
row = list(df.iloc[evt.index[0]])
|
||||
log.debug(f'Huggingface: selected={row} index={evt.index}')
|
||||
return row[0] # repo_id only
|
||||
|
||||
|
||||
def hf_download_model(hub_id: str, token, variant, revision, mirror, custom_pipeline):
|
||||
|
|
@ -71,11 +77,11 @@ def hf_download_model(hub_id: str, token, variant, revision, mirror, custom_pipe
|
|||
download_diffusers_model(hub_id, cache_dir=opts.diffusers_dir, token=token, variant=variant, revision=revision, mirror=mirror, custom_pipeline=custom_pipeline)
|
||||
from modules.sd_models import list_models # pylint: disable=W0621
|
||||
list_models()
|
||||
log.info(f'Diffuser model downloaded: model="{hub_id}"')
|
||||
log.info(f'Huggingface: model="{hub_id}" downloaded')
|
||||
return f'Diffuser model downloaded: model="{hub_id}"'
|
||||
|
||||
|
||||
def hf_update_token(token):
|
||||
log.debug('Huggingface update token')
|
||||
log.debug('Huggingface: update token')
|
||||
opts.huggingface_token = token
|
||||
opts.save()
|
||||
|
|
|
|||
|
|
@ -397,13 +397,13 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
|||
shared.state.batch_no = n + 1
|
||||
debug(f'Processing inner: iteration={n+1}/{p.n_iter}')
|
||||
p.iteration = n
|
||||
if shared.state.interrupted:
|
||||
shared.log.debug(f'Process interrupted: {n+1}/{p.n_iter}')
|
||||
break
|
||||
if shared.state.skipped:
|
||||
shared.log.debug(f'Process skipped: {n+1}/{p.n_iter}')
|
||||
shared.state.skipped = False
|
||||
continue
|
||||
if shared.state.interrupted:
|
||||
shared.log.debug(f'Process interrupted: {n+1}/{p.n_iter}')
|
||||
break
|
||||
|
||||
if not hasattr(p, 'keep_prompts'):
|
||||
p.prompts = p.all_prompts[n * p.batch_size:(n+1) * p.batch_size]
|
||||
|
|
@ -441,6 +441,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
|||
infotexts = [create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, index=0)]
|
||||
else:
|
||||
samples = []
|
||||
if not shared.opts.keep_incomplete:
|
||||
break
|
||||
|
||||
if p.scripts is not None and isinstance(p.scripts, scripts_manager.ScriptRunner):
|
||||
p.scripts.postprocess_batch(p, samples, batch_number=n)
|
||||
|
|
@ -460,6 +462,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
|||
if shared.cmd_opts.lowvram:
|
||||
devices.torch_gc(force=True, reason='lowvram')
|
||||
timer.process.record('post')
|
||||
if shared.state.interrupted:
|
||||
break
|
||||
|
||||
if not p.xyz:
|
||||
if hasattr(shared.sd_model, 'restore_pipeline') and (shared.sd_model.restore_pipeline is not None):
|
||||
|
|
|
|||
|
|
@ -53,9 +53,9 @@ def diffusers_callback_legacy(step: int, timestep: int, latents: typing.Union[to
|
|||
|
||||
def diffusers_callback(pipe, step: int = 0, timestep: int = 0, kwargs: dict = {}):
|
||||
t0 = time.time()
|
||||
if devices.backend == "ipex": # xe driver on linux needs this
|
||||
if devices.backend == "ipex":
|
||||
torch.xpu.synchronize(devices.device)
|
||||
elif (devices.backend == "zluda") or (devices.backend == "rocm") or (devices.backend == "cuda"):
|
||||
elif devices.backend in {"cuda", "zluda", "rocm"}:
|
||||
torch.cuda.synchronize(devices.device)
|
||||
latents = kwargs.get('latents', None)
|
||||
if debug:
|
||||
|
|
|
|||
|
|
@ -47,7 +47,7 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=No
|
|||
"Sampler": p.sampler_name if p.sampler_name != 'Default' else None,
|
||||
"Seed": all_seeds[index],
|
||||
"Seed resize from": None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}",
|
||||
"CFG scale": p.cfg_scale if p.cfg_scale > 1.0 else None,
|
||||
"CFG scale": p.cfg_scale if p.cfg_scale > 1.0 else 1.0,
|
||||
"CFG rescale": p.diffusers_guidance_rescale if p.diffusers_guidance_rescale > 0 else None,
|
||||
"CFG end": p.cfg_end if p.cfg_end < 1.0 else None,
|
||||
"CFG true": p.pag_scale if p.pag_scale > 0 else None,
|
||||
|
|
|
|||
|
|
@ -216,7 +216,7 @@ def taesd_vae_decode(latents):
|
|||
t0 = time.time()
|
||||
if len(latents) == 0:
|
||||
return []
|
||||
if shared.opts.diffusers_vae_slicing and len(latents) > 1:
|
||||
if len(latents) > 1:
|
||||
decoded = torch.zeros((len(latents), 3, latents.shape[2] * 8, latents.shape[3] * 8), dtype=devices.dtype_vae, device=devices.device)
|
||||
for i in range(latents.shape[0]):
|
||||
decoded[i] = sd_vae_taesd.decode(latents[i])
|
||||
|
|
|
|||
|
|
@ -96,7 +96,7 @@ class Agent:
|
|||
self.blaslt_supported = os.path.exists(os.path.join(blaslt_tensile_libpath, f"Kernels.so-000-{name}.hsaco" if sys.platform == "win32" else f"extop_{name}.co"))
|
||||
|
||||
@property
|
||||
def therock(self) -> str:
|
||||
def therock(self) -> Union[str, None]:
|
||||
if (self.gfx_version & 0xFFF0) == 0x1100:
|
||||
return "gfx110X-dgpu"
|
||||
if self.gfx_version == 0x1151:
|
||||
|
|
@ -107,7 +107,7 @@ class Agent:
|
|||
return "gfx94X-dcgpu"
|
||||
if self.gfx_version == 0x950:
|
||||
return "gfx950-dcgpu"
|
||||
raise RuntimeError(f"Unsupported GPU architecture: {self.name}")
|
||||
return None
|
||||
|
||||
def get_gfx_version(self) -> Union[str, None]:
|
||||
if self.gfx_version >= 0x1100 and self.gfx_version < 0x1200:
|
||||
|
|
@ -207,28 +207,47 @@ def get_flash_attention_command(agent: Agent) -> str:
|
|||
return "--no-build-isolation " + os.environ.get("FLASH_ATTENTION_PACKAGE", default)
|
||||
|
||||
|
||||
def refresh():
|
||||
global environment, blaslt_tensile_libpath, is_installed, version # pylint: disable=global-statement
|
||||
if sys.platform == "win32":
|
||||
global agents # pylint: disable=global-statement
|
||||
try:
|
||||
agents = driver_get_agents()
|
||||
except Exception:
|
||||
agents = []
|
||||
environment = find()
|
||||
if environment is not None:
|
||||
if isinstance(environment, ROCmEnvironment):
|
||||
blaslt_tensile_libpath = os.environ.get("HIPBLASLT_TENSILE_LIBPATH", os.path.join(environment.path, "bin" if sys.platform == "win32" else "lib", "hipblaslt", "library"))
|
||||
is_installed = True
|
||||
version = get_version()
|
||||
|
||||
|
||||
if sys.platform == "win32":
|
||||
def get_agents() -> List[Agent]:
|
||||
if isinstance(environment, ROCmEnvironment):
|
||||
out = spawn("amdgpu-arch", cwd=os.path.join(environment.path, 'bin'))
|
||||
else:
|
||||
# Assume that amdgpu-arch is in PATH (venv/Scripts/amdgpu-arch.exe)
|
||||
out = spawn("amdgpu-arch")
|
||||
out = out.strip()
|
||||
return [Agent(x.split(' ')[-1].strip()) for x in out.split("\n")]
|
||||
return agents
|
||||
#if isinstance(environment, ROCmEnvironment):
|
||||
# out = spawn("amdgpu-arch", cwd=os.path.join(environment.path, 'bin'))
|
||||
#else:
|
||||
# # Assume that amdgpu-arch is in PATH (venv/Scripts/amdgpu-arch.exe)
|
||||
# out = spawn("amdgpu-arch")
|
||||
#out = out.strip()
|
||||
#if out == "":
|
||||
# return []
|
||||
#return [Agent(x.split(' ')[-1].strip()) for x in out.split("\n")]
|
||||
|
||||
def driver_get_agents() -> List[Agent]:
|
||||
# unsafe and experimental feature
|
||||
from modules import windows_hip_ffi
|
||||
hip = windows_hip_ffi.HIP()
|
||||
count = hip.get_device_count()
|
||||
agents = [None] * count
|
||||
_agents = [None] * count
|
||||
for i in range(count):
|
||||
prop = hip.get_device_properties(i)
|
||||
name = prop.gcnArchName.decode('utf-8').strip('\x00')
|
||||
agents[i] = Agent(name)
|
||||
_agents[i] = Agent(name)
|
||||
del hip
|
||||
return agents
|
||||
return _agents
|
||||
|
||||
def postinstall():
|
||||
import torch
|
||||
|
|
@ -243,6 +262,14 @@ if sys.platform == "win32":
|
|||
os.environ["PATH"] = ";".join(paths_no_rocm)
|
||||
return
|
||||
|
||||
build_targets = torch.cuda.get_arch_list()
|
||||
for available in agents:
|
||||
if available.name in build_targets:
|
||||
return
|
||||
|
||||
# use cpu instead of crashing
|
||||
torch.cuda.is_available = lambda: False
|
||||
|
||||
def rocm_init():
|
||||
try:
|
||||
import torch
|
||||
|
|
@ -275,15 +302,16 @@ if sys.platform == "win32":
|
|||
return True, None
|
||||
|
||||
is_wsl: bool = False
|
||||
else:
|
||||
agents: List[Agent] = [] # temp
|
||||
else: # sys.platform != "win32"
|
||||
def get_agents() -> List[Agent]:
|
||||
try:
|
||||
agents = spawn("rocm_agent_enumerator").split("\n")
|
||||
agents = [x for x in agents if x and x != 'gfx000']
|
||||
_agents = spawn("rocm_agent_enumerator").split("\n")
|
||||
_agents = [x for x in _agents if x and x != 'gfx000']
|
||||
except Exception: # old version of ROCm WSL doesn't have rocm_agent_enumerator
|
||||
agents = spawn("rocminfo").split("\n")
|
||||
agents = [x.strip().split(" ")[-1] for x in agents if x.startswith(' Name:') and "CPU" not in x]
|
||||
return [Agent(x) for x in agents]
|
||||
_agents = spawn("rocminfo").split("\n")
|
||||
_agents = [x.strip().split(" ")[-1] for x in _agents if x.startswith(' Name:') and "CPU" not in x]
|
||||
return [Agent(x) for x in _agents]
|
||||
|
||||
def postinstall():
|
||||
if is_wsl:
|
||||
|
|
@ -300,17 +328,9 @@ else:
|
|||
return True, None
|
||||
|
||||
is_wsl: bool = os.environ.get('WSL_DISTRO_NAME', 'unknown' if spawn('wslpath -w /') else None) is not None
|
||||
|
||||
environment = None
|
||||
blaslt_tensile_libpath = ""
|
||||
is_installed = False
|
||||
version = None
|
||||
|
||||
def refresh():
|
||||
global environment, blaslt_tensile_libpath, is_installed, version # pylint: disable=global-statement
|
||||
environment = find()
|
||||
if environment is not None:
|
||||
if isinstance(environment, ROCmEnvironment):
|
||||
blaslt_tensile_libpath = os.environ.get("HIPBLASLT_TENSILE_LIBPATH", os.path.join(environment.path, "bin" if sys.platform == "win32" else "lib", "hipblaslt", "library"))
|
||||
is_installed = True
|
||||
version = get_version()
|
||||
refresh()
|
||||
|
|
|
|||
|
|
@ -51,7 +51,7 @@ def find_sampler_config(name):
|
|||
def restore_default(model):
|
||||
if model is None:
|
||||
return None
|
||||
if getattr(model, "default_scheduler", None) is not None:
|
||||
if getattr(model, "default_scheduler", None) is not None and getattr(model, "scheduler", None) is not None:
|
||||
model.scheduler = copy.deepcopy(model.default_scheduler)
|
||||
if hasattr(model, "prior_pipe") and hasattr(model.prior_pipe, "scheduler"):
|
||||
model.prior_pipe.scheduler = copy.deepcopy(model.default_scheduler)
|
||||
|
|
|
|||
|
|
@ -29,7 +29,7 @@ def conv_fp8_matmul_tensorwise(
|
|||
if svd_up is not None:
|
||||
input = input.flatten(0,-2)
|
||||
if bias is not None:
|
||||
bias = torch.addmm(bias, torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
|
||||
bias = torch.addmm(bias.to(dtype=svd_down.dtype), torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
|
||||
else:
|
||||
bias = torch.mm(torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
|
||||
|
||||
|
|
|
|||
|
|
@ -32,7 +32,7 @@ def conv_int8_matmul(
|
|||
if svd_up is not None:
|
||||
input = input.flatten(0,-2)
|
||||
if bias is not None:
|
||||
bias = torch.addmm(bias, torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
|
||||
bias = torch.addmm(bias.to(dtype=svd_down.dtype), torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
|
||||
else:
|
||||
bias = torch.mm(torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
|
||||
|
||||
|
|
|
|||
|
|
@ -31,7 +31,7 @@ def fp8_matmul_tensorwise(
|
|||
if svd_up is not None:
|
||||
input.flatten(0,-2)
|
||||
if bias is not None:
|
||||
bias = torch.addmm(bias, torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
|
||||
bias = torch.addmm(bias.to(dtype=svd_down.dtype), torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
|
||||
else:
|
||||
bias = torch.mm(torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
|
||||
dummy_input_scale = torch.ones(1, device=input.device, dtype=torch.float32)
|
||||
|
|
|
|||
|
|
@ -36,7 +36,7 @@ def int8_matmul(
|
|||
if svd_up is not None:
|
||||
input = input.flatten(0,-2)
|
||||
if bias is not None:
|
||||
bias = torch.addmm(bias, torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
|
||||
bias = torch.addmm(bias.to(dtype=svd_down.dtype), torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
|
||||
else:
|
||||
bias = torch.mm(torch.mm(input.to(dtype=svd_down.dtype), svd_down), svd_up)
|
||||
input, scale = quantize_int8_matmul_input(input, scale)
|
||||
|
|
|
|||
|
|
@ -199,10 +199,13 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
|
|||
layer.weight.data = layer.weight.to(dtype=torch.float32)
|
||||
|
||||
if use_svd:
|
||||
layer.weight.data, svd_up, svd_down = apply_svdquant(layer.weight, rank=svd_rank, niter=svd_steps)
|
||||
if use_quantized_matmul:
|
||||
svd_up = svd_up.t_()
|
||||
svd_down = svd_down.t_()
|
||||
try:
|
||||
layer.weight.data, svd_up, svd_down = apply_svdquant(layer.weight, rank=svd_rank, niter=svd_steps)
|
||||
if use_quantized_matmul:
|
||||
svd_up = svd_up.t_()
|
||||
svd_down = svd_down.t_()
|
||||
except Exception:
|
||||
svd_up, svd_down = None, None
|
||||
else:
|
||||
svd_up, svd_down = None, None
|
||||
|
||||
|
|
@ -210,9 +213,9 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
|
|||
if use_quantized_matmul and dtype_dict[weights_dtype]["num_bits"] >= 6:
|
||||
group_size = -1
|
||||
elif is_linear_type:
|
||||
group_size = 2 ** ((2 if not use_svd else 3) + dtype_dict[weights_dtype]["num_bits"])
|
||||
group_size = 2 ** ((2 if svd_up is None else 3) + dtype_dict[weights_dtype]["num_bits"])
|
||||
else:
|
||||
group_size = 2 ** ((1 if not use_svd else 2) + dtype_dict[weights_dtype]["num_bits"])
|
||||
group_size = 2 ** ((1 if svd_up is None else 2) + dtype_dict[weights_dtype]["num_bits"])
|
||||
elif use_quantized_matmul and dtype_dict[weights_dtype]["num_bits"] == 8:
|
||||
group_size = -1 # override user value, re-quantizing 8bit into 8bit is pointless
|
||||
elif group_size != -1 and not is_linear_type:
|
||||
|
|
|
|||
|
|
@ -194,7 +194,7 @@ class State:
|
|||
def begin(self, title="", task_id=0, api=None):
|
||||
import modules.devices
|
||||
self.clear()
|
||||
self.interrupted = False
|
||||
self.interrupted = self.interrupted if title.startswith('Save') else False
|
||||
self.skipped = False
|
||||
self.job_history += 1
|
||||
self.total_jobs += 1
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@ def create_guidance_inputs(tab):
|
|||
guidance_btn = ui_components.ToolButton(value=ui_symbols.book, elem_id=f"{tab}_guider_docs")
|
||||
guidance_btn.click(fn=None, _js='getGuidanceDocs', inputs=[guidance_name], outputs=[])
|
||||
with gr.Row(visible=shared.opts.model_modular_enable):
|
||||
guidance_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.1, label='_Guidance scale', value=6.0, elem_id=f"{tab}_guidance_scale")
|
||||
guidance_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.1, label='_Guidance scale', value=6.0, elem_id=f"{tab}_guidance_scale")
|
||||
guidance_rescale = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='_Guidance rescale', value=0.0, elem_id=f"{tab}_guidance_rescale")
|
||||
with gr.Row(visible=shared.opts.model_modular_enable):
|
||||
guidance_start = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='_Guidance start', value=0.0, elem_id=f"{tab}_guidance_start")
|
||||
|
|
@ -114,7 +114,7 @@ def create_guidance_inputs(tab):
|
|||
|
||||
gr.HTML(value='<br><h2>Fallback guidance</h2>', visible=shared.opts.model_modular_enable, elem_id=f"{tab}_guidance_note")
|
||||
with gr.Row(elem_id=f"{tab}_cfg_row", elem_classes=['flexbox']):
|
||||
cfg_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.1, label='Guidance scale', value=6.0, elem_id=f"{tab}_cfg_scale")
|
||||
cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.1, label='Guidance scale', value=6.0, elem_id=f"{tab}_cfg_scale")
|
||||
cfg_end = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='Guidance end', value=1.0, elem_id=f"{tab}_cfg_end")
|
||||
with gr.Row():
|
||||
diffusers_guidance_rescale = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Guidance rescale', value=0.0, elem_id=f"{tab}_image_cfg_rescale")
|
||||
|
|
|
|||
|
|
@ -561,7 +561,7 @@ def create_ui():
|
|||
with gr.Row():
|
||||
hf_headers = ['Name', 'Pipeline', 'Tags', 'Downloads', 'Updated', 'URL']
|
||||
hf_types = ['str', 'str', 'str', 'number', 'date', 'markdown']
|
||||
hf_results = gr.DataFrame(None, label='Search results', show_label=True, interactive=False, wrap=True, headers=hf_headers, datatype=hf_types, type='array')
|
||||
hf_results = gr.DataFrame(None, label='Search results', show_label=True, interactive=False, wrap=True, headers=hf_headers, datatype=hf_types)
|
||||
|
||||
hf_search_text.submit(fn=hf_search, inputs=[hf_search_text], outputs=[hf_results])
|
||||
hf_search_btn.click(fn=hf_search, inputs=[hf_search_text], outputs=[hf_results])
|
||||
|
|
|
|||
|
|
@ -27,15 +27,24 @@ def create_ui():
|
|||
with gr.Row(elem_id="video_interface", equal_height=False):
|
||||
with gr.Tabs(elem_classes=['video-tabs'], elem_id='video-tabs'):
|
||||
overrides = ui_common.create_override_inputs('video')
|
||||
with gr.Tab('Core', id='video-tab') as video_tab:
|
||||
with gr.Tab('Size', id='video-size-tab') as _video_size_tab:
|
||||
from modules.video_models import video_ui
|
||||
video_ui.create_ui(prompt, negative, styles, overrides)
|
||||
width, height, frames, seed, reuse_seed = video_ui.create_ui_size()
|
||||
with gr.Tab('Inputs', id='video-inputs-tab') as _video_inputs_tab:
|
||||
from modules.video_models import video_ui
|
||||
init_image, init_strength, last_image = video_ui.create_ui_inputs()
|
||||
with gr.Tab('Video Output', id='video-outputs-tab') as _video_outputs_tab:
|
||||
from modules.video_models import video_ui
|
||||
mp4_fps, mp4_interpolate, mp4_codec, mp4_ext, mp4_opt, mp4_video, mp4_frames, mp4_sf = video_ui.create_ui_outputs()
|
||||
with gr.Tab('Models', id='video-core-tab') as video_core_tab:
|
||||
from modules.video_models import video_ui
|
||||
video_ui.create_ui(prompt, negative, styles, overrides, init_image, init_strength, last_image, mp4_fps, mp4_interpolate, mp4_codec, mp4_ext, mp4_opt, mp4_video, mp4_frames, mp4_sf, width, height, frames, seed, reuse_seed)
|
||||
with gr.Tab('FramePack', id='framepack-tab') as framepack_tab:
|
||||
from modules.framepack import framepack_ui
|
||||
framepack_ui.create_ui(prompt, negative, styles, overrides)
|
||||
framepack_ui.create_ui(prompt, negative, styles, overrides, init_image, last_image, mp4_fps, mp4_interpolate, mp4_codec, mp4_ext, mp4_opt, mp4_video, mp4_frames, mp4_sf)
|
||||
with gr.Tab('LTX', id='ltx-tab') as ltx_tab:
|
||||
from modules.ltx import ltx_ui
|
||||
ltx_ui.create_ui(prompt, negative, styles, overrides)
|
||||
ltx_ui.create_ui(prompt, negative, styles, overrides, init_image, init_strength, last_image, mp4_fps, mp4_interpolate, mp4_codec, mp4_ext, mp4_opt, mp4_video, mp4_frames, mp4_sf, width, height, frames, seed)
|
||||
|
||||
paste_fields = [
|
||||
(prompt, "Prompt"), # cannot add more fields as they are not defined yet
|
||||
|
|
@ -45,7 +54,7 @@ def create_ui():
|
|||
generation_parameters_copypaste.register_paste_params_button(bindings)
|
||||
|
||||
current_tab = gr.Textbox(visible=False, value='video')
|
||||
video_tab.select(fn=lambda: 'video', inputs=[], outputs=[current_tab])
|
||||
video_core_tab.select(fn=lambda: 'video', inputs=[], outputs=[current_tab])
|
||||
framepack_tab.select(fn=lambda: 'framepack', inputs=[], outputs=[current_tab])
|
||||
ltx_tab.select(fn=lambda: 'ltx', inputs=[], outputs=[current_tab])
|
||||
|
||||
|
|
|
|||
|
|
@ -64,7 +64,7 @@ class Upscaler:
|
|||
scaler.custom = True
|
||||
scalers.append(scaler)
|
||||
loaded.append(file_name)
|
||||
shared.log.debug(f'Upscaler type={self.name} folder="{folder}" model="{model_name}" path="{file_name}"')
|
||||
# shared.log.debug(f'Upscaler type={self.name} folder="{folder}" model="{model_name}" path="{file_name}"')
|
||||
|
||||
def find_scalers(self):
|
||||
scalers = []
|
||||
|
|
|
|||
|
|
@ -349,4 +349,24 @@ models = {
|
|||
te_cls=transformers.T5EncoderModel,
|
||||
dit_cls=diffusers.CosmosTransformer3DModel),
|
||||
],
|
||||
'Kandinsky': [
|
||||
Model(name='Kandinsky 5.0 Lite SFT T2V',
|
||||
url='https://huggingface.co/ai-forever/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers',
|
||||
repo='ai-forever/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers',
|
||||
repo_cls=diffusers.Kandinsky5T2VPipeline,
|
||||
te_cls=transformers.Qwen2_5_VLForConditionalGeneration,
|
||||
dit_cls=diffusers.Kandinsky5Transformer3DModel),
|
||||
Model(name='Kandinsky 5.0 Lite CFG-distilled T2V',
|
||||
url='https://huggingface.co/ai-forever/Kandinsky-5.0-T2V-Lite-nocfg-5s-Diffusers',
|
||||
repo='ai-forever/Kandinsky-5.0-T2V-Lite-nocfg-5s-Diffusers',
|
||||
repo_cls=diffusers.Kandinsky5T2VPipeline,
|
||||
te_cls=transformers.Qwen2_5_VLForConditionalGeneration,
|
||||
dit_cls=diffusers.Kandinsky5Transformer3DModel),
|
||||
Model(name='Kandinsky 5.0 Lite Steps-distilled T2V',
|
||||
url='https://huggingface.co/ai-forever/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers',
|
||||
repo='ai-forever/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers',
|
||||
repo_cls=diffusers.Kandinsky5T2VPipeline,
|
||||
te_cls=transformers.Qwen2_5_VLForConditionalGeneration,
|
||||
dit_cls=diffusers.Kandinsky5Transformer3DModel),
|
||||
],
|
||||
}
|
||||
|
|
|
|||
|
|
@ -39,6 +39,10 @@ def load_model(selected: models_def.Model):
|
|||
selected.te = 'hunyuanvideo-community/HunyuanVideo'
|
||||
selected.te_folder = 'text_encoder'
|
||||
selected.te_revision = None
|
||||
if selected.te_cls.__name__ == 'Qwen2_5_VLForConditionalGeneration' and shared.opts.te_shared_t5:
|
||||
selected.te = 'ai-forever/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers'
|
||||
selected.te_folder = 'text_encoder'
|
||||
selected.te_revision = None
|
||||
|
||||
shared.log.debug(f'Video load: module=te repo="{selected.te or selected.repo}" folder="{selected.te_folder}" cls={selected.te_cls.__name__} quant={model_quant.get_quant_type(quant_args)}')
|
||||
kwargs["text_encoder"] = selected.te_cls.from_pretrained(
|
||||
|
|
@ -104,7 +108,7 @@ def load_model(selected: models_def.Model):
|
|||
shared.sd_model.sd_model_hash = None
|
||||
sd_models.set_diffuser_options(shared.sd_model, offload=False)
|
||||
|
||||
decode, text, image, slicing, tiling = False, False, False, False, False
|
||||
decode, text, image, slicing, tiling, framewise = False, False, False, False, False, False
|
||||
if selected.vae_hijack and hasattr(shared.sd_model.vae, 'decode'):
|
||||
sd_hijack_vae.init_hijack(shared.sd_model)
|
||||
decode = True
|
||||
|
|
@ -115,6 +119,9 @@ def load_model(selected: models_def.Model):
|
|||
shared.sd_model.orig_encode_image = shared.sd_model.encode_image
|
||||
shared.sd_model.encode_image = video_utils.hijack_encode_image
|
||||
image = True
|
||||
if hasattr(shared.sd_model, 'vae') and hasattr(shared.sd_model.vae, 'use_framewise_decoding'):
|
||||
shared.sd_model.vae.use_framewise_decoding = True
|
||||
framewise = True
|
||||
if hasattr(shared.sd_model, 'vae') and hasattr(shared.sd_model.vae, 'enable_slicing'):
|
||||
shared.sd_model.vae.enable_slicing()
|
||||
slicing = True
|
||||
|
|
@ -130,6 +137,6 @@ def load_model(selected: models_def.Model):
|
|||
loaded_model = selected.name
|
||||
msg = f'Video load: cls={shared.sd_model.__class__.__name__} model="{selected.name}" time={t1-t0:.2f}'
|
||||
shared.log.info(msg)
|
||||
shared.log.debug(f'Video hijacks: decode={decode} text={text} image={image} slicing={slicing} tiling={tiling}')
|
||||
shared.log.debug(f'Video hijacks: decode={decode} text={text} image={image} slicing={slicing} tiling={tiling} framewise={framewise}')
|
||||
shared.state.end(jobid)
|
||||
return msg
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
import os
|
||||
import copy
|
||||
import time
|
||||
from modules import shared, errors, sd_models, processing, devices, images, ui_common
|
||||
from modules.video_models import models_def, video_utils, video_load, video_vae, video_overrides, video_save, video_prompt
|
||||
|
|
@ -92,10 +93,21 @@ def generate(*args, **kwargs):
|
|||
p.task_args['height'] = p.height
|
||||
p.task_args['output_type'] = 'latent' if (p.vae_type == 'Remote') else 'pil'
|
||||
p.ops.append('video')
|
||||
|
||||
# set scheduler params
|
||||
orig_dynamic_shift = shared.opts.schedulers_dynamic_shift
|
||||
orig_sampler_shift = shared.opts.schedulers_shift
|
||||
shared.opts.data['schedulers_dynamic_shift'] = dynamic_shift
|
||||
shared.opts.data['schedulers_shift'] = sampler_shift
|
||||
if hasattr(shared.sd_model.scheduler, 'config') and hasattr(shared.sd_model.scheduler, 'register_to_config'):
|
||||
if hasattr(shared.sd_model.scheduler.config, 'use_dynamic_shifting'):
|
||||
shared.sd_model.scheduler.config.use_dynamic_shifting = dynamic_shift
|
||||
shared.sd_model.scheduler.register_to_config(use_dynamic_shifting = dynamic_shift)
|
||||
if hasattr(shared.sd_model.scheduler.config, 'flow_shift'):
|
||||
shared.sd_model.scheduler.config.flow_shift = sampler_shift
|
||||
shared.sd_model.scheduler.register_to_config(flow_shift = sampler_shift)
|
||||
shared.sd_model.default_scheduler = copy.deepcopy(shared.sd_model.scheduler)
|
||||
|
||||
video_overrides.set_overrides(p, selected)
|
||||
debug(f'Video: task_args={p.task_args}')
|
||||
|
||||
|
|
|
|||
|
|
@ -72,10 +72,55 @@ def run_video(*args):
|
|||
return video_run.generate(*args)
|
||||
elif selected and 'anisora' in selected.name.lower():
|
||||
return video_run.generate(*args)
|
||||
elif selected and 'Kandinsky' in selected.name:
|
||||
return video_run.generate(*args)
|
||||
return video_utils.queue_err(f'model not found: engine="{engine}" model="{model}"')
|
||||
|
||||
|
||||
def create_ui(prompt, negative, styles, overrides):
|
||||
def create_ui_inputs():
|
||||
with gr.Row():
|
||||
with gr.Column(variant='compact', elem_id="video_inputs", elem_classes=['settings-column'], scale=1):
|
||||
init_strength = gr.Slider(label='Init strength', minimum=0.0, maximum=1.0, step=0.01, value=0.8, elem_id="video_denoising_strength")
|
||||
gr.HTML("<br>  Init image")
|
||||
init_image = gr.Image(elem_id="video_image", show_label=False, type="pil", image_mode="RGB", width=256, height=256)
|
||||
gr.HTML("<br>  Last image")
|
||||
last_image = gr.Image(elem_id="video_last", show_label=False, type="pil", image_mode="RGB", width=256, height=256)
|
||||
return init_image, init_strength, last_image
|
||||
|
||||
|
||||
def create_ui_outputs():
|
||||
with gr.Row():
|
||||
with gr.Column(variant='compact', elem_id="video_outputs", elem_classes=['settings-column'], scale=1):
|
||||
with gr.Row():
|
||||
mp4_fps = gr.Slider(label="FPS", minimum=1, maximum=60, value=24, step=1)
|
||||
mp4_interpolate = gr.Slider(label="Video interpolation", minimum=0, maximum=10, value=0, step=1)
|
||||
with gr.Row():
|
||||
mp4_codec = gr.Dropdown(label="Video codec", choices=['none', 'libx264'], value='libx264', type='value')
|
||||
ui_common.create_refresh_button(mp4_codec, video_utils.get_codecs, elem_id="framepack_mp4_codec_refresh")
|
||||
mp4_ext = gr.Textbox(label="Video format", value='mp4', elem_id="framepack_mp4_ext")
|
||||
mp4_opt = gr.Textbox(label="Video options", value='crf:16', elem_id="framepack_mp4_ext")
|
||||
with gr.Row():
|
||||
mp4_video = gr.Checkbox(label='Video save video', value=True, elem_id="framepack_mp4_video")
|
||||
mp4_frames = gr.Checkbox(label='Video save frames', value=False, elem_id="framepack_mp4_frames")
|
||||
mp4_sf = gr.Checkbox(label='Video save safetensors', value=False, elem_id="framepack_mp4_sf")
|
||||
return mp4_fps, mp4_interpolate, mp4_codec, mp4_ext, mp4_opt, mp4_video, mp4_frames, mp4_sf
|
||||
|
||||
|
||||
def create_ui_size():
|
||||
with gr.Row():
|
||||
with gr.Column(variant='compact', elem_id="video_size", elem_classes=['settings-column'], scale=1):
|
||||
with gr.Row():
|
||||
width, height = ui_sections.create_resolution_inputs('video', default_width=832, default_height=480)
|
||||
with gr.Row():
|
||||
frames = gr.Slider(label='Frames', minimum=1, maximum=1024, step=1, value=17, elem_id="video_frames")
|
||||
seed = gr.Number(label='Initial seed', value=-1, elem_id="video_seed", container=True)
|
||||
random_seed = ToolButton(ui_symbols.random, elem_id="video_seed_random")
|
||||
reuse_seed = ToolButton(ui_symbols.reuse, elem_id="video_seed_reuse")
|
||||
random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
|
||||
return width, height, frames, seed, reuse_seed
|
||||
|
||||
|
||||
def create_ui(prompt, negative, styles, overrides, init_image, init_strength, last_image, mp4_fps, mp4_interpolate, mp4_codec, mp4_ext, mp4_opt, mp4_video, mp4_frames, mp4_sf, width, height, frames, seed, reuse_seed):
|
||||
with gr.Row():
|
||||
with gr.Column(variant='compact', elem_id="video_settings", elem_classes=['settings-column'], scale=1):
|
||||
with gr.Row():
|
||||
|
|
@ -86,14 +131,6 @@ def create_ui(prompt, negative, styles, overrides):
|
|||
btn_load = ToolButton(ui_symbols.loading, elem_id="video_model_load")
|
||||
with gr.Row():
|
||||
url = gr.HTML(label='Model URL', elem_id='video_model_url', value='<br><br>')
|
||||
with gr.Accordion(open=True, label="Size", elem_id='video_size_accordion'):
|
||||
with gr.Row():
|
||||
width, height = ui_sections.create_resolution_inputs('video', default_width=832, default_height=480)
|
||||
with gr.Row():
|
||||
frames = gr.Slider(label='Frames', minimum=1, maximum=1024, step=1, value=17, elem_id="video_frames")
|
||||
seed = gr.Number(label='Initial seed', value=-1, elem_id="video_seed", container=True)
|
||||
random_seed = ToolButton(ui_symbols.random, elem_id="video_seed_random")
|
||||
reuse_seed = ToolButton(ui_symbols.reuse, elem_id="video_seed_reuse")
|
||||
with gr.Accordion(open=False, label="Parameters", elem_id='video_parameters_accordion'):
|
||||
steps, sampler_index = ui_sections.create_sampler_and_steps_selection(None, "video", default_steps=50)
|
||||
with gr.Row():
|
||||
|
|
@ -106,30 +143,9 @@ def create_ui(prompt, negative, styles, overrides):
|
|||
with gr.Row():
|
||||
vae_type = gr.Dropdown(label='VAE decode', choices=['Default', 'Tiny', 'Remote'], value='Default', elem_id="video_vae_type")
|
||||
vae_tile_frames = gr.Slider(label='Tile frames', minimum=1, maximum=64, step=1, value=16, elem_id="video_vae_tile_frames")
|
||||
with gr.Accordion(open=False, label="Init image", elem_id='video_init_accordion'):
|
||||
init_strength = gr.Slider(label='Init strength', minimum=0.0, maximum=1.0, step=0.01, value=0.5, elem_id="video_denoising_strength")
|
||||
gr.HTML("<br>  Init image")
|
||||
init_image = gr.Image(elem_id="video_image", show_label=False, type="pil", image_mode="RGB", width=256, height=256)
|
||||
gr.HTML("<br>  Last image")
|
||||
last_image = gr.Image(elem_id="video_last", show_label=False, type="pil", image_mode="RGB", width=256, height=256)
|
||||
|
||||
vlm_enhance, vlm_model, vlm_system_prompt = ui_video_vlm.create_ui(prompt_element=prompt, image_element=init_image)
|
||||
|
||||
with gr.Accordion(label="Video", open=False, elem_id='video_output_accordion'):
|
||||
with gr.Row():
|
||||
mp4_fps = gr.Slider(label="FPS", minimum=1, maximum=60, value=24, step=1)
|
||||
mp4_interpolate = gr.Slider(label="Video interpolation", minimum=0, maximum=10, value=0, step=1)
|
||||
with gr.Row():
|
||||
mp4_codec = gr.Dropdown(label="Video codec", choices=['none', 'libx264'], value='libx264', type='value')
|
||||
ui_common.create_refresh_button(mp4_codec, video_utils.get_codecs, elem_id="framepack_mp4_codec_refresh")
|
||||
mp4_ext = gr.Textbox(label="Video format", value='mp4', elem_id="framepack_mp4_ext")
|
||||
mp4_opt = gr.Textbox(label="Video options", value='crf:16', elem_id="framepack_mp4_ext")
|
||||
with gr.Row():
|
||||
mp4_video = gr.Checkbox(label='Video save video', value=True, elem_id="framepack_mp4_video")
|
||||
mp4_frames = gr.Checkbox(label='Video save frames', value=False, elem_id="framepack_mp4_frames")
|
||||
mp4_sf = gr.Checkbox(label='Video save safetensors', value=False, elem_id="framepack_mp4_sf")
|
||||
|
||||
|
||||
# output panel with gallery and video tabs
|
||||
with gr.Column(elem_id='video-output-column', scale=2) as _column_output:
|
||||
with gr.Tabs(elem_classes=['video-output-tabs'], elem_id='video-output-tabs'):
|
||||
|
|
@ -140,7 +156,6 @@ def create_ui(prompt, negative, styles, overrides):
|
|||
|
||||
# connect reuse seed button
|
||||
ui_common.connect_reuse_seed(seed, reuse_seed, gen_info, is_subseed=False)
|
||||
random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
|
||||
# handle engine and model change
|
||||
engine.change(fn=engine_change, inputs=[engine], outputs=[model])
|
||||
model.change(fn=model_change, inputs=[engine, model], outputs=[url])
|
||||
|
|
|
|||
|
|
@ -9,7 +9,9 @@ vae_type = None
|
|||
def set_vae_params(p):
|
||||
global vae_type # pylint: disable=global-statement
|
||||
vae_type = p.vae_type
|
||||
if p.vae_tile_frames > p.frames:
|
||||
if hasattr(shared.sd_model.vae, 'enable_slicing'):
|
||||
shared.sd_model.vae.enable_slicing()
|
||||
if p.frames > p.vae_tile_frames:
|
||||
if hasattr(shared.sd_model.vae, 'tile_sample_min_num_frames'):
|
||||
shared.sd_model.vae.tile_sample_min_num_frames = p.vae_tile_frames
|
||||
if hasattr(shared.sd_model.vae, 'use_framewise_decoding'):
|
||||
|
|
@ -30,6 +32,8 @@ def vae_decode_tiny(latents):
|
|||
variant = 'TAE MochiVideo'
|
||||
elif 'WAN' in shared.sd_model.__class__.__name__:
|
||||
variant = 'TAE WanVideo'
|
||||
elif 'Kandinsky' in shared.sd_model.__class__.__name__:
|
||||
variant = 'TAE HunyuanVideo'
|
||||
else:
|
||||
shared.log.warning(f'Decode: type=Tiny cls={shared.sd_model.__class__.__name__} not supported')
|
||||
return None
|
||||
|
|
|
|||
|
|
@ -16,6 +16,7 @@ if sys.platform == "win32":
|
|||
def __init__(self):
|
||||
ctypes.windll.kernel32.LoadLibraryA.restype = ctypes.wintypes.HMODULE
|
||||
ctypes.windll.kernel32.LoadLibraryA.argtypes = [ctypes.c_char_p]
|
||||
self.handle = None
|
||||
path = os.environ.get("windir", "C:\\Windows") + "\\System32\\amdhip64_6.dll"
|
||||
if not os.path.isfile(path):
|
||||
path = os.environ.get("windir", "C:\\Windows") + "\\System32\\amdhip64_7.dll"
|
||||
|
|
@ -32,6 +33,8 @@ if sys.platform == "win32":
|
|||
ctypes.windll.kernel32.GetProcAddress(self.handle, b"hipGetDeviceProperties"))
|
||||
|
||||
def __del__(self):
|
||||
if self.handle is None:
|
||||
return
|
||||
# Hopefully this will prevent conflicts with amdhip64_7.dll from ROCm Python packages or HIP SDK
|
||||
ctypes.windll.kernel32.FreeLibrary.argtypes = [ctypes.wintypes.HMODULE]
|
||||
ctypes.windll.kernel32.FreeLibrary(self.handle)
|
||||
|
|
|
|||
|
|
@ -251,7 +251,7 @@ class GenerationExperiment:
|
|||
try:
|
||||
path = self.save_heat_map(word, tokenizer, crop=crop)
|
||||
path_map[word] = path
|
||||
except:
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return path_map
|
||||
|
|
@ -328,7 +328,7 @@ class GenerationExperiment:
|
|||
vocab=vocab,
|
||||
subtype=directory.name
|
||||
))
|
||||
except:
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return experiments
|
||||
|
|
|
|||
2
wiki
2
wiki
|
|
@ -1 +1 @@
|
|||
Subproject commit 78e4e6b94adac848c118b85d7a4c552babcb1acd
|
||||
Subproject commit 847eae26c796ae32b8cd74b0cc79b705bfdc8f54
|
||||
Loading…
Reference in New Issue