the mixed_precision ops can have input_scale parameters that are used
in tensor math but arent a weight or bias so dont get proper VRAM
management. Treat these as force-castable parameters like the non comfy
weight, random params are buffers already are.
Revert CheckpointLoaderSimple to its original form (no device input)
so it remains the simple default loader.
Add new CheckpointLoaderDevice node (advanced/loaders) with separate
model_device, clip_device, and vae_device inputs for per-component
GPU placement in multi-GPU setups.
Amp-Thread-ID: https://ampcode.com/threads/T-019dabdc-8feb-766f-b4dc-f46ef4d8ff57
Co-authored-by: Amp <amp@ampcode.com>
Add torch.cuda.set_device() calls to match model's load device in:
- CLIP.encode_from_tokens: fixes 'Can't export tensors on a different
CUDA device index' when CLIP is loaded on a non-default GPU
- CLIP.encode_from_tokens_scheduled: same fix for the hooks code path
- CLIP.generate: same fix for text generation
- VAE.decode: fixes VAE decoding on non-default GPU
- VAE.encode: fixes VAE encoding on non-default GPU
Same pattern as the existing outer_sample fix in samplers.py - saves
and restores previous CUDA device in a try/finally block.
Amp-Thread-ID: https://ampcode.com/threads/T-019dabdc-8feb-766f-b4dc-f46ef4d8ff57
Co-authored-by: Amp <amp@ampcode.com>
- resolve_gpu_device_option: reject negative indices (gpu:-1)
- UNETLoader: set offload_device when cpu is selected
- CheckpointLoaderSimple: pass te_model_options for CLIP device,
set offload_device for cpu, pass load_device to VAE
- load_diffusion_model_state_dict: respect offload_device from model_options
- load_state_dict_guess_config: respect offload_device, pass load_device to VAE
Amp-Thread-ID: https://ampcode.com/threads/T-019daa41-f394-731a-8955-4cff4f16283a
Co-authored-by: Amp <amp@ampcode.com>
Custom CUDA kernels (comfy_kitchen fp8 quantization) use
torch.cuda.current_device() for DLPack tensor export. When a model is
loaded on a non-default GPU (e.g. cuda:1), the CUDA context must match
or the kernel fails with 'Can't export tensors on a different CUDA
device index'. Save and restore the previous device around sampling.
Amp-Thread-ID: https://ampcode.com/threads/T-019daa41-f394-731a-8955-4cff4f16283a
Co-authored-by: Amp <amp@ampcode.com>
When a workflow saved on a 2-GPU machine (with device=gpu:1) is loaded
on a 1-GPU machine, the combo validation would reject the unknown value.
VALIDATE_INPUTS with the device parameter bypasses combo validation for
that input only, allowing resolve_gpu_device_option to handle the
graceful fallback at runtime.
Amp-Thread-ID: https://ampcode.com/threads/T-019daa41-f394-731a-8955-4cff4f16283a
Co-authored-by: Amp <amp@ampcode.com>
- Add get_gpu_device_options() and resolve_gpu_device_option() helpers
in model_management.py for vendor-agnostic GPU device selection
- Add device widget to CheckpointLoaderSimple, UNETLoader, VAELoader
- Expand device options in CLIPLoader, DualCLIPLoader, LTXAVTextEncoderLoader
from [default, cpu] to include gpu:0, gpu:1, etc. on multi-GPU systems
- Wire load_diffusion_model_state_dict and load_state_dict_guess_config
to respect model_options['load_device']
- Graceful fallback: unrecognized devices (e.g. gpu:1 on single-GPU)
silently fall back to default
Amp-Thread-ID: https://ampcode.com/threads/T-019daa41-f394-731a-8955-4cff4f16283a
Co-authored-by: Amp <amp@ampcode.com>
Skip unnecessary clone of inference-mode tensors when already inside
torch.inference_mode(), matching the existing guard in set_attr_param.
The unconditional clone introduced in 20561aa9 caused transient VRAM
doubling during model movement for FP8/quantized models.
Rattus
Jedrzej Kosinski
comfyanonymous
Abdul Rehman
Jukka Seppänen
comfyanonymous
Alexander Piskun
Daxiong (Lin)
rattus
Bedovyy
Jun Yamog
Comfy Org PR Bot
Terry Jia