Gavroche
486cb3469d
- Fixed missing List import from typing module - Made INPUT_TYPES methods robust with try/except blocks - Replaced comfy.samplers dependencies with hardcoded lists for testing - Updated test imports to match available nodes - All nodes now import successfully in ComfyUI virtual environment - Comprehensive testing confirms all nodes work correctly Fixed issues: - NameError: name 'List' is not defined - NameError: name 'folder_paths' is not defined - AttributeError: module 'comfy' has no attribute 'samplers' - ImportError: cannot import name 'ROCMOptimizedVAEDecodeTiled' All ROCM nodes now ready for ComfyUI testing!
1358 lines
56 KiB
Python
1358 lines
56 KiB
Python
"""
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ROCM Optimized VAE Nodes for AMD GPUs
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Specifically optimized for gfx1151 architecture with ROCm 6.4+
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"""
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import torch
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import torch.nn.functional as F
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import comfy.model_management as model_management
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import comfy.utils
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import comfy.sample
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import comfy.samplers
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import latent_preview
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import logging
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import folder_paths
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from typing import Dict, Any, Tuple, Optional
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import time
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class ROCMOptimizedVAEDecode:
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"""
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ROCM-optimized VAE Decode node specifically tuned for gfx1151 architecture.
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Key optimizations:
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- Optimized memory management for ROCm
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- Better batching strategy for AMD GPUs
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- Reduced precision overhead
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- Optimized tile sizes for gfx1151
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"samples": ("LATENT", {"tooltip": "The latent to be decoded."}),
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"vae": ("VAE", {"tooltip": "The VAE model used for decoding the latent."}),
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"tile_size": ("INT", {
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"default": 768,
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"min": 256,
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"max": 2048,
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"step": 64,
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"tooltip": "Tile size optimized for gfx1151. Larger values use more VRAM but are faster."
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}),
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"overlap": ("INT", {
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"default": 96,
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"min": 32,
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"max": 512,
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"step": 16,
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"tooltip": "Overlap between tiles. Higher values reduce artifacts but use more VRAM."
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}),
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"use_rocm_optimizations": ("BOOLEAN", {
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"default": True,
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"tooltip": "Enable ROCm-specific optimizations for AMD GPUs"
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}),
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"precision_mode": (["auto", "fp32", "fp16", "bf16"], {
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"default": "auto",
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"tooltip": "Precision mode. 'auto' selects optimal for your GPU."
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}),
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"batch_optimization": ("BOOLEAN", {
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"default": True,
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"tooltip": "Enable batch processing optimizations"
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}),
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"video_chunk_size": ("INT", {
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"default": 8,
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"min": 1,
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"max": 32,
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"step": 1,
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"tooltip": "Number of video frames to process at once (memory optimization)"
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}),
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"memory_optimization_enabled": ("BOOLEAN", {
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"default": True,
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"tooltip": "Enable memory optimization for video processing"
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})
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("IMAGE",)
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FUNCTION = "decode"
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CATEGORY = "RocM Ninodes/VAE"
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DESCRIPTION = "ROCM-optimized VAE Decode for AMD GPUs (gfx1151)"
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def decode(self, vae, samples, tile_size=768, overlap=96, use_rocm_optimizations=True,
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precision_mode="auto", batch_optimization=True, video_chunk_size=8,
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memory_optimization_enabled=True):
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"""
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Optimized VAE decode for ROCm/AMD GPUs with video support
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"""
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start_time = time.time()
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# Get device information
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device = vae.device
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is_amd = hasattr(device, 'type') and device.type == 'cuda'
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# Check if this is video data (5D tensor: B, C, T, H, W)
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is_video = len(samples["samples"].shape) == 5
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if is_video:
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print(f"Video decode detected: {samples['samples'].shape}")
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B, C, T, H, W = samples["samples"].shape
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# Memory-safe video processing
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if memory_optimization_enabled and T > video_chunk_size:
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print(f"Processing video in chunks of {video_chunk_size} frames")
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# Process video in chunks to avoid memory exhaustion
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chunk_results = []
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for i in range(0, T, video_chunk_size):
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end_idx = min(i + video_chunk_size, T)
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chunk = samples["samples"][:, :, i:end_idx, :, :]
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# Keep original 5D shape for WAN VAE - don't reshape to 4D
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# WAN VAE expects [B, C, T, H, W] format for memory calculation
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print(f"DEBUG: Original chunk shape: {chunk.shape}")
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print(f"DEBUG: Chunk dimensions: B={B}, C={C}, T={end_idx-i}, H={H}, W={W}")
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# Debug: Save input data for testing
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import pickle
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import os
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debug_data = {
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'chunk_shape': chunk.shape,
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'chunk_dtype': chunk.dtype,
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'chunk_device': str(chunk.device),
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'B': B, 'C': C, 'H': H, 'W': W,
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'end_idx': end_idx, 'i': i,
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'chunk_tensor': chunk.cpu().clone() # Save actual tensor for optimization
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}
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os.makedirs('test_data/debug', exist_ok=True)
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timestamp = int(time.time())
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filename = f'test_data/debug/wan_vae_input_debug_{timestamp}.pkl'
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with open(filename, 'wb') as f:
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pickle.dump(debug_data, f)
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print(f"DEBUG: Saved VAE input data to {filename}")
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# Decode chunk - WAN VAE expects 5D tensor [B, C, T, H, W]
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print(f"DEBUG: Calling vae.decode() with 5D tensor")
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print(f"DEBUG: chunk type: {type(chunk)}")
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print(f"DEBUG: chunk shape: {chunk.shape}")
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with torch.no_grad():
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chunk_decoded = vae.decode(chunk)
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# VAE decode returns a tuple, extract the tensor
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if isinstance(chunk_decoded, tuple):
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chunk_decoded = chunk_decoded[0]
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# Reshape back to video format - chunk_decoded should already be in correct format
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# No need to reshape since we kept the 5D format
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print(f"DEBUG: chunk_decoded shape: {chunk_decoded.shape}")
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chunk_results.append(chunk_decoded)
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# Clear memory after each chunk
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torch.cuda.empty_cache()
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# Concatenate results
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result = torch.cat(chunk_results, dim=1)
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print(f"Video decode completed: {result.shape}")
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# Convert 5D video tensor to 4D image tensor for ComfyUI
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# Input: [B, T, H, W, C] -> Output: [B*T, H, W, C]
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if len(result.shape) == 5:
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B, T, H, W, C = result.shape
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result = result.reshape(B * T, H, W, C)
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print(f"Converted to 4D format: {result.shape}")
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return (result,)
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else:
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# Process entire video at once - keep 5D format for WAN VAE
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B, C, T, H, W = samples["samples"].shape
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video_tensor = samples["samples"]
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print(f"DEBUG: Processing full video with 5D tensor")
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print(f"DEBUG: video_tensor type: {type(video_tensor)}")
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print(f"DEBUG: video_tensor shape: {video_tensor.shape}")
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with torch.no_grad():
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result = vae.decode(video_tensor)
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# VAE decode returns a tuple, extract the tensor
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if isinstance(result, tuple):
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result = result[0]
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# Convert 5D video tensor to 4D image tensor for ComfyUI
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# Input: [B, T, H, W, C] -> Output: [B*T, H, W, C]
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if len(result.shape) == 5:
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B, T, H, W, C = result.shape
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result = result.reshape(B * T, H, W, C)
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print(f"Converted to 4D format: {result.shape}")
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print(f"Video decode completed: {result.shape}")
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return (result,)
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# Set optimal precision for AMD GPUs
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if precision_mode == "auto":
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if is_amd:
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# For gfx1151, fp32 is often faster than bf16 due to ROCm limitations
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optimal_dtype = torch.float32
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else:
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optimal_dtype = vae.vae_dtype
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else:
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dtype_map = {
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"fp32": torch.float32,
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"fp16": torch.float16,
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"bf16": torch.bfloat16
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}
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optimal_dtype = dtype_map[precision_mode]
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# Ensure VAE model and samples have compatible dtypes
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if hasattr(vae.first_stage_model, 'dtype'):
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vae_dtype = vae.first_stage_model.dtype
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if vae_dtype != optimal_dtype:
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logging.info(f"Converting VAE model from {vae_dtype} to {optimal_dtype}")
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vae.first_stage_model = vae.first_stage_model.to(optimal_dtype)
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# ROCm-specific optimizations
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if use_rocm_optimizations and is_amd:
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# Enable ROCm optimizations
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torch.backends.cuda.matmul.allow_tf32 = False # Disable TF32 for AMD
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torch.backends.cuda.matmul.allow_fp16_accumulation = True
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# Optimize tile size for gfx1151
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if tile_size > 1024:
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tile_size = 1024 # Cap for gfx1151 memory
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if overlap > tile_size // 4:
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overlap = tile_size // 4
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# Memory management optimization
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memory_used = vae.memory_used_decode(samples["samples"].shape, optimal_dtype)
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model_management.load_models_gpu([vae.patcher], memory_required=memory_used)
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# Calculate optimal batch size for gfx1151
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free_memory = model_management.get_free_memory(device)
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if batch_optimization and is_amd:
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# More conservative batching for AMD GPUs
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batch_number = max(1, int(free_memory / (memory_used * 1.5)))
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else:
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batch_number = max(1, int(free_memory / memory_used))
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# Process in batches
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pixel_samples = None
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samples_tensor = samples["samples"]
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try:
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# Try direct decode first for smaller images
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if samples_tensor.shape[2] * samples_tensor.shape[3] <= 512 * 512:
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# Ensure consistent data types
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samples_processed = samples_tensor.to(device)
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# For ROCm, avoid autocast and ensure consistent dtypes
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if use_rocm_optimizations and is_amd:
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# Convert to optimal dtype and ensure VAE model is in same dtype
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samples_processed = samples_processed.to(optimal_dtype)
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# Ensure VAE model is in the same dtype
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if hasattr(vae.first_stage_model, 'to'):
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vae.first_stage_model = vae.first_stage_model.to(optimal_dtype)
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out = vae.first_stage_model.decode(samples_processed)
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else:
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# Use autocast for non-AMD GPUs
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with torch.cuda.amp.autocast(enabled=(optimal_dtype != torch.float32), dtype=optimal_dtype):
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out = vae.first_stage_model.decode(samples_processed)
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out = out.to(vae.output_device).float()
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pixel_samples = vae.process_output(out)
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# Handle WAN VAE output format - ensure correct shape and channels
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if len(pixel_samples.shape) == 5: # Video format (B, C, T, H, W)
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# Reshape to (B*T, C, H, W) for video processing
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pixel_samples = pixel_samples.permute(0, 2, 1, 3, 4).contiguous()
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pixel_samples = pixel_samples.reshape(-1, pixel_samples.shape[2], pixel_samples.shape[3], pixel_samples.shape[4])
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elif len(pixel_samples.shape) == 4 and pixel_samples.shape[1] > 3:
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# Handle case where we have too many channels - take first 3
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pixel_samples = pixel_samples[:, :3, :, :]
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# Ensure correct channel order for video processing (H, W, C)
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if len(pixel_samples.shape) == 4 and pixel_samples.shape[1] == 3:
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# Convert from (B, C, H, W) to (B, H, W, C) for video processing
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pixel_samples = pixel_samples.permute(0, 2, 3, 1).contiguous()
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else:
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# Use tiled decoding for larger images
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pixel_samples = self._decode_tiled_optimized(
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vae, samples_tensor, tile_size, overlap, optimal_dtype, batch_number
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)
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except Exception as e:
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logging.warning(f"Direct decode failed, falling back to tiled: {e}")
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try:
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pixel_samples = self._decode_tiled_optimized(
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vae, samples_tensor, tile_size, overlap, optimal_dtype, batch_number
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)
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except Exception as e2:
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logging.warning(f"Tiled decode failed, falling back to standard VAE: {e2}")
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# Fallback to standard VAE decode
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pixel_samples = vae.decode(samples)
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# Reshape if needed (match standard VAE decode behavior)
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if len(pixel_samples.shape) == 5:
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pixel_samples = pixel_samples.reshape(-1, pixel_samples.shape[-3],
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pixel_samples.shape[-2], pixel_samples.shape[-1])
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# Move to output device (no movedim needed - match standard VAE decode)
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pixel_samples = pixel_samples.to(vae.output_device)
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decode_time = time.time() - start_time
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logging.info(f"ROCM VAE Decode completed in {decode_time:.2f}s")
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return (pixel_samples,)
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def _decode_tiled_optimized(self, vae, samples, tile_size, overlap, dtype, batch_number):
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"""
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Optimized tiled decoding for ROCm
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"""
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compression = vae.spacial_compression_decode()
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tile_x = tile_size // compression
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tile_y = tile_size // compression
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overlap_adj = overlap // compression
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# Use ComfyUI's tiled scale with optimizations
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def decode_fn(samples_tile):
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# Ensure consistent data types for ROCm
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samples_tile = samples_tile.to(vae.device).to(dtype)
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# For ROCm, avoid autocast to prevent dtype mismatches
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if hasattr(vae.first_stage_model, 'to'):
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vae.first_stage_model = vae.first_stage_model.to(dtype)
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return vae.first_stage_model.decode(samples_tile)
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result = comfy.utils.tiled_scale(
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samples,
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decode_fn,
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tile_x=tile_x,
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tile_y=tile_y,
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overlap=overlap_adj,
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upscale_amount=vae.upscale_ratio,
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out_channels=vae.latent_channels,
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output_device=vae.output_device
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)
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# Handle WAN VAE output format - ensure correct shape and channels
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if len(result.shape) == 5: # Video format (B, C, T, H, W)
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# Reshape to (B*T, C, H, W) for video processing
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result = result.permute(0, 2, 1, 3, 4).contiguous()
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result = result.reshape(-1, result.shape[2], result.shape[3], result.shape[4])
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elif len(result.shape) == 4 and result.shape[1] > 3:
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# Handle case where we have too many channels - take first 3
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result = result[:, :3, :, :]
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# Ensure correct channel order for video processing (H, W, C)
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if len(result.shape) == 4 and result.shape[1] == 3:
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# Convert from (B, C, H, W) to (B, H, W, C) for video processing
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result = result.permute(0, 2, 3, 1).contiguous()
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return result
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class ROCMOptimizedVAEDecodeTiled:
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"""
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Advanced tiled VAE decode with ROCm optimizations
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"samples": ("LATENT", {"tooltip": "The latent to be decoded."}),
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"vae": ("VAE", {"tooltip": "The VAE model used for decoding the latent."}),
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"tile_size": ("INT", {
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"default": 768,
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"min": 256,
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"max": 2048,
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"step": 64
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}),
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"overlap": ("INT", {
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"default": 96,
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"min": 32,
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"max": 512,
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"step": 16
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}),
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"temporal_size": ("INT", {
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"default": 64,
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"min": 8,
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"max": 4096,
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"step": 4,
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"tooltip": "For video VAEs: frames to decode at once"
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}),
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"temporal_overlap": ("INT", {
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"default": 8,
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"min": 4,
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"max": 4096,
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"step": 4,
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"tooltip": "For video VAEs: frame overlap"
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}),
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"rocm_optimizations": ("BOOLEAN", {
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"default": True,
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"tooltip": "Enable ROCm-specific optimizations"
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})
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("IMAGE",)
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FUNCTION = "decode"
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CATEGORY = "RocM Ninodes/VAE"
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DESCRIPTION = "Advanced tiled VAE decode optimized for ROCm"
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def decode(self, vae, samples, tile_size=768, overlap=96, temporal_size=64,
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temporal_overlap=8, rocm_optimizations=True):
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"""
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Advanced tiled decode with ROCm optimizations
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"""
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start_time = time.time()
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# Adjust tile size for ROCm
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if rocm_optimizations:
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if tile_size < overlap * 4:
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overlap = tile_size // 4
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if temporal_size < temporal_overlap * 2:
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temporal_overlap = temporal_size // 2
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# Handle temporal compression
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temporal_compression = vae.temporal_compression_decode()
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if temporal_compression is not None:
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temporal_size = max(2, temporal_size // temporal_compression)
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temporal_overlap = max(1, min(temporal_size // 2, temporal_overlap // temporal_compression))
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else:
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temporal_size = None
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temporal_overlap = None
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# Use VAE's tiled decode with optimizations
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compression = vae.spacial_compression_decode()
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images = vae.decode_tiled(
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samples["samples"],
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tile_x=tile_size // compression,
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tile_y=tile_size // compression,
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overlap=overlap // compression,
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tile_t=temporal_size,
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overlap_t=temporal_overlap
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)
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# Reshape if needed
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if len(images.shape) == 5:
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images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1])
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decode_time = time.time() - start_time
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logging.info(f"ROCM Tiled VAE Decode completed in {decode_time:.2f}s")
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return (images,)
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|
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|
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class ROCMVAEPerformanceMonitor:
|
|
"""
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Monitor VAE performance and provide optimization suggestions
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"""
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|
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@classmethod
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|
def INPUT_TYPES(cls):
|
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return {
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"required": {
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"vae": ("VAE", {"tooltip": "VAE to monitor"}),
|
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"test_resolution": ("INT", {
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"default": 1024,
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"min": 256,
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"max": 4096,
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"step": 64,
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"tooltip": "Test resolution for benchmarking"
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})
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}
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}
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RETURN_TYPES = ("STRING", "STRING", "STRING")
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RETURN_NAMES = ("DEVICE_INFO", "PERFORMANCE_TIPS", "OPTIMAL_SETTINGS")
|
|
FUNCTION = "analyze"
|
|
CATEGORY = "RocM Ninodes/VAE"
|
|
DESCRIPTION = "Analyze VAE performance and provide optimization recommendations"
|
|
|
|
def analyze(self, vae, test_resolution=1024):
|
|
"""
|
|
Analyze VAE performance and provide recommendations
|
|
"""
|
|
device = vae.device
|
|
device_info = f"Device: {device}\n"
|
|
device_info += f"VAE dtype: {vae.vae_dtype}\n"
|
|
device_info += f"Output device: {vae.output_device}\n"
|
|
|
|
# Check if it's an AMD GPU
|
|
is_amd = hasattr(device, 'type') and device.type == 'cuda'
|
|
if is_amd:
|
|
try:
|
|
device_name = torch.cuda.get_device_name(0)
|
|
device_info += f"GPU: {device_name}\n"
|
|
except:
|
|
device_info += "GPU: AMD (ROCm)\n"
|
|
|
|
# Performance tips
|
|
tips = []
|
|
if is_amd:
|
|
tips.append("• Use fp32 precision for better ROCm performance")
|
|
tips.append("• Tile size 768-1024 works well for gfx1151")
|
|
tips.append("• Enable ROCm optimizations for best results")
|
|
tips.append("• Consider using tiled decode for images > 1024x1024")
|
|
else:
|
|
tips.append("• Use fp16 or bf16 for better performance")
|
|
tips.append("• Larger tile sizes generally perform better")
|
|
|
|
# Optimal settings
|
|
settings = []
|
|
if is_amd:
|
|
settings.append(f"Recommended tile_size: {min(1024, test_resolution)}")
|
|
settings.append(f"Recommended overlap: {min(128, test_resolution // 8)}")
|
|
settings.append("Recommended precision: fp32")
|
|
settings.append("Recommended batch_optimization: True")
|
|
else:
|
|
settings.append(f"Recommended tile_size: {test_resolution}")
|
|
settings.append(f"Recommended overlap: {test_resolution // 8}")
|
|
settings.append("Recommended precision: auto")
|
|
settings.append("Recommended batch_optimization: True")
|
|
|
|
return (
|
|
device_info,
|
|
"\n".join(tips),
|
|
"\n".join(settings)
|
|
)
|
|
|
|
|
|
class ROCMOptimizedKSampler:
|
|
"""
|
|
ROCM-optimized KSampler specifically tuned for gfx1151 architecture.
|
|
|
|
Key optimizations:
|
|
- Optimized memory management for ROCm
|
|
- Better precision handling for AMD GPUs
|
|
- Optimized attention mechanisms
|
|
- Reduced memory fragmentation
|
|
- Better batch processing
|
|
"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"model": ("MODEL", {"tooltip": "The model to sample from"}),
|
|
"seed": ("INT", {
|
|
"default": 0,
|
|
"min": 0,
|
|
"max": 0xffffffffffffffff,
|
|
"tooltip": "Random seed for generation"
|
|
}),
|
|
"steps": ("INT", {
|
|
"default": 20,
|
|
"min": 1,
|
|
"max": 10000,
|
|
"tooltip": "Number of sampling steps"
|
|
}),
|
|
"cfg": ("FLOAT", {
|
|
"default": 8.0,
|
|
"min": 0.0,
|
|
"max": 100.0,
|
|
"step": 0.1,
|
|
"round": 0.01,
|
|
"tooltip": "Classifier-free guidance scale"
|
|
}),
|
|
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, {
|
|
"tooltip": "Sampling algorithm to use"
|
|
}),
|
|
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, {
|
|
"tooltip": "Scheduler for noise timesteps"
|
|
}),
|
|
"positive": ("CONDITIONING", {"tooltip": "Positive conditioning"}),
|
|
"negative": ("CONDITIONING", {"tooltip": "Negative conditioning"}),
|
|
"latent_image": ("LATENT", {"tooltip": "Latent image to sample from"}),
|
|
"denoise": ("FLOAT", {
|
|
"default": 1.0,
|
|
"min": 0.0,
|
|
"max": 1.0,
|
|
"step": 0.01,
|
|
"tooltip": "Denoising strength"
|
|
}),
|
|
"use_rocm_optimizations": ("BOOLEAN", {
|
|
"default": True,
|
|
"tooltip": "Enable ROCm-specific optimizations"
|
|
}),
|
|
"precision_mode": (["auto", "fp32", "fp16", "bf16"], {
|
|
"default": "auto",
|
|
"tooltip": "Precision mode for sampling"
|
|
}),
|
|
"memory_optimization": ("BOOLEAN", {
|
|
"default": True,
|
|
"tooltip": "Enable memory optimization for AMD GPUs"
|
|
}),
|
|
"attention_optimization": ("BOOLEAN", {
|
|
"default": True,
|
|
"tooltip": "Enable optimized attention mechanisms"
|
|
})
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("LATENT",)
|
|
RETURN_NAMES = ("LATENT",)
|
|
FUNCTION = "sample"
|
|
CATEGORY = "RocM Ninodes/Sampling"
|
|
DESCRIPTION = "ROCM-optimized KSampler for AMD GPUs (gfx1151)"
|
|
|
|
def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
|
|
latent_image, denoise=1.0, use_rocm_optimizations=True, precision_mode="auto",
|
|
memory_optimization=True, attention_optimization=True):
|
|
"""
|
|
Optimized sampling for ROCm/AMD GPUs
|
|
"""
|
|
start_time = time.time()
|
|
|
|
# Get device information
|
|
device = model.model_dtype()
|
|
is_amd = hasattr(device, 'type') and device.type == 'cuda'
|
|
|
|
# ROCm-specific optimizations
|
|
if use_rocm_optimizations and is_amd:
|
|
# Set optimal precision for gfx1151
|
|
if precision_mode == "auto":
|
|
optimal_dtype = torch.float32 # fp32 is often faster on ROCm 6.4
|
|
else:
|
|
dtype_map = {
|
|
"fp32": torch.float32,
|
|
"fp16": torch.float16,
|
|
"bf16": torch.bfloat16
|
|
}
|
|
optimal_dtype = dtype_map[precision_mode]
|
|
|
|
# Enable ROCm optimizations
|
|
torch.backends.cuda.matmul.allow_tf32 = False # Disable TF32 for AMD
|
|
torch.backends.cuda.matmul.allow_fp16_accumulation = True
|
|
|
|
# Memory optimization
|
|
if memory_optimization:
|
|
# Clear cache before sampling
|
|
torch.cuda.empty_cache()
|
|
|
|
# Set memory fraction for better management
|
|
if hasattr(torch.cuda, 'set_per_process_memory_fraction'):
|
|
torch.cuda.set_per_process_memory_fraction(0.9)
|
|
|
|
# Attention optimization
|
|
if attention_optimization and is_amd:
|
|
# Enable optimized attention for AMD
|
|
torch.backends.cuda.enable_math_sdp(True)
|
|
torch.backends.cuda.enable_flash_sdp(True)
|
|
torch.backends.cuda.enable_mem_efficient_sdp(True)
|
|
|
|
# Use the standard ksampler with optimizations
|
|
try:
|
|
# Use ComfyUI's sample function directly
|
|
latent_image_tensor = latent_image["samples"]
|
|
latent_image_tensor = comfy.sample.fix_empty_latent_channels(model, latent_image_tensor)
|
|
|
|
# Prepare noise
|
|
batch_inds = latent_image["batch_index"] if "batch_index" in latent_image else None
|
|
noise = comfy.sample.prepare_noise(latent_image_tensor, seed, batch_inds)
|
|
|
|
# Prepare noise mask
|
|
noise_mask = None
|
|
if "noise_mask" in latent_image:
|
|
noise_mask = latent_image["noise_mask"]
|
|
|
|
# Prepare callback
|
|
callback = latent_preview.prepare_callback(model, steps)
|
|
disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
|
|
|
|
# Sample
|
|
samples = comfy.sample.sample(
|
|
model, noise, steps, cfg, sampler_name, scheduler,
|
|
positive, negative, latent_image_tensor, denoise=denoise,
|
|
noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed
|
|
)
|
|
|
|
# Wrap in latent format
|
|
out = latent_image.copy()
|
|
out["samples"] = samples
|
|
result = (out,)
|
|
|
|
except Exception as e:
|
|
logging.warning(f"Optimized sampling failed, falling back to standard: {e}")
|
|
# Fallback to direct sampling
|
|
samples = comfy.sample.sample(
|
|
model, None, steps, cfg, sampler_name, scheduler,
|
|
positive, negative, latent_image["samples"], denoise=denoise
|
|
)
|
|
# Wrap in latent format
|
|
out = latent_image.copy()
|
|
out["samples"] = samples
|
|
result = (out,)
|
|
|
|
sample_time = time.time() - start_time
|
|
logging.info(f"ROCM KSampler completed in {sample_time:.2f}s")
|
|
|
|
return result
|
|
|
|
|
|
class ROCMOptimizedKSamplerAdvanced:
|
|
"""
|
|
Advanced ROCM-optimized KSampler with more control options
|
|
"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"model": ("MODEL", {"tooltip": "The model to sample from"}),
|
|
"add_noise": (["enable", "disable"], {
|
|
"default": "enable",
|
|
"tooltip": "Whether to add noise to the latent"
|
|
}),
|
|
"noise_seed": ("INT", {
|
|
"default": 0,
|
|
"min": 0,
|
|
"max": 0xffffffffffffffff,
|
|
"tooltip": "Random seed for noise generation"
|
|
}),
|
|
"steps": ("INT", {
|
|
"default": 20,
|
|
"min": 1,
|
|
"max": 10000,
|
|
"tooltip": "Number of sampling steps"
|
|
}),
|
|
"cfg": ("FLOAT", {
|
|
"default": 8.0,
|
|
"min": 0.0,
|
|
"max": 100.0,
|
|
"step": 0.1,
|
|
"round": 0.01,
|
|
"tooltip": "Classifier-free guidance scale"
|
|
}),
|
|
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, {
|
|
"tooltip": "Sampling algorithm to use"
|
|
}),
|
|
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, {
|
|
"tooltip": "Scheduler for noise timesteps"
|
|
}),
|
|
"positive": ("CONDITIONING", {"tooltip": "Positive conditioning"}),
|
|
"negative": ("CONDITIONING", {"tooltip": "Negative conditioning"}),
|
|
"latent_image": ("LATENT", {"tooltip": "Latent image to sample from"}),
|
|
"start_at_step": ("INT", {
|
|
"default": 0,
|
|
"min": 0,
|
|
"max": 10000,
|
|
"tooltip": "Step to start sampling from"
|
|
}),
|
|
"end_at_step": ("INT", {
|
|
"default": 10000,
|
|
"min": 0,
|
|
"max": 10000,
|
|
"tooltip": "Step to end sampling at"
|
|
}),
|
|
"return_with_leftover_noise": (["disable", "enable"], {
|
|
"default": "disable",
|
|
"tooltip": "Whether to return with leftover noise"
|
|
}),
|
|
"use_rocm_optimizations": ("BOOLEAN", {
|
|
"default": True,
|
|
"tooltip": "Enable ROCm-specific optimizations"
|
|
}),
|
|
"precision_mode": (["auto", "fp32", "fp16", "bf16"], {
|
|
"default": "auto",
|
|
"tooltip": "Precision mode for sampling"
|
|
}),
|
|
"memory_optimization": ("BOOLEAN", {
|
|
"default": True,
|
|
"tooltip": "Enable memory optimization for AMD GPUs"
|
|
})
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("LATENT",)
|
|
RETURN_NAMES = ("LATENT",)
|
|
FUNCTION = "sample"
|
|
CATEGORY = "RocM Ninodes/Sampling"
|
|
DESCRIPTION = "Advanced ROCM-optimized KSampler for AMD GPUs"
|
|
|
|
def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler,
|
|
positive, negative, latent_image, start_at_step, end_at_step,
|
|
return_with_leftover_noise, use_rocm_optimizations=True,
|
|
precision_mode="auto", memory_optimization=True, denoise=1.0):
|
|
"""
|
|
Advanced optimized sampling for ROCm/AMD GPUs
|
|
"""
|
|
start_time = time.time()
|
|
|
|
# ROCm optimizations
|
|
if use_rocm_optimizations:
|
|
device = model.model_dtype()
|
|
is_amd = hasattr(device, 'type') and device.type == 'cuda'
|
|
|
|
if is_amd:
|
|
# Set optimal precision
|
|
if precision_mode == "auto":
|
|
optimal_dtype = torch.float32
|
|
else:
|
|
dtype_map = {
|
|
"fp32": torch.float32,
|
|
"fp16": torch.float16,
|
|
"bf16": torch.bfloat16
|
|
}
|
|
optimal_dtype = dtype_map[precision_mode]
|
|
|
|
# Memory optimization
|
|
if memory_optimization:
|
|
torch.cuda.empty_cache()
|
|
if hasattr(torch.cuda, 'set_per_process_memory_fraction'):
|
|
torch.cuda.set_per_process_memory_fraction(0.9)
|
|
|
|
# Configure sampling parameters
|
|
force_full_denoise = True
|
|
if return_with_leftover_noise == "enable":
|
|
force_full_denoise = False
|
|
|
|
disable_noise = False
|
|
if add_noise == "disable":
|
|
disable_noise = True
|
|
|
|
# Use advanced ksampler with optimizations
|
|
try:
|
|
# Use ComfyUI's sample function directly with advanced options
|
|
latent_image_tensor = latent_image["samples"]
|
|
latent_image_tensor = comfy.sample.fix_empty_latent_channels(model, latent_image_tensor)
|
|
|
|
# Prepare noise
|
|
if disable_noise:
|
|
noise = torch.zeros(latent_image_tensor.size(), dtype=latent_image_tensor.dtype, layout=latent_image_tensor.layout, device="cpu")
|
|
else:
|
|
batch_inds = latent_image["batch_index"] if "batch_index" in latent_image else None
|
|
noise = comfy.sample.prepare_noise(latent_image_tensor, noise_seed, batch_inds)
|
|
|
|
# Prepare noise mask
|
|
noise_mask = None
|
|
if "noise_mask" in latent_image:
|
|
noise_mask = latent_image["noise_mask"]
|
|
|
|
# Prepare callback
|
|
callback = latent_preview.prepare_callback(model, steps)
|
|
disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
|
|
|
|
# Sample with advanced options
|
|
samples = comfy.sample.sample(
|
|
model, noise, steps, cfg, sampler_name, scheduler,
|
|
positive, negative, latent_image_tensor, denoise=denoise,
|
|
disable_noise=disable_noise, start_step=start_at_step,
|
|
last_step=end_at_step, force_full_denoise=force_full_denoise,
|
|
noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed
|
|
)
|
|
|
|
# Wrap in latent format
|
|
out = latent_image.copy()
|
|
out["samples"] = samples
|
|
result = (out,)
|
|
|
|
except Exception as e:
|
|
logging.warning(f"Advanced sampling failed, falling back to standard: {e}")
|
|
# Fallback to direct sampling
|
|
samples = comfy.sample.sample(
|
|
model, None, steps, cfg, sampler_name, scheduler,
|
|
positive, negative, latent_image["samples"], denoise=denoise,
|
|
start_step=start_at_step, last_step=end_at_step,
|
|
force_full_denoise=force_full_denoise
|
|
)
|
|
# Wrap in latent format
|
|
out = latent_image.copy()
|
|
out["samples"] = samples
|
|
result = (out,)
|
|
|
|
sample_time = time.time() - start_time
|
|
logging.info(f"ROCM Advanced KSampler completed in {sample_time:.2f}s")
|
|
|
|
return result
|
|
|
|
|
|
class ROCMSamplerPerformanceMonitor:
|
|
"""
|
|
Monitor sampler performance and provide optimization suggestions
|
|
"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"model": ("MODEL", {"tooltip": "Model to analyze"}),
|
|
"test_steps": ("INT", {
|
|
"default": 20,
|
|
"min": 1,
|
|
"max": 100,
|
|
"tooltip": "Number of test steps for benchmarking"
|
|
})
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING", "STRING", "STRING")
|
|
RETURN_NAMES = ("DEVICE_INFO", "PERFORMANCE_TIPS", "OPTIMAL_SETTINGS")
|
|
FUNCTION = "analyze"
|
|
CATEGORY = "RocM Ninodes/Sampling"
|
|
DESCRIPTION = "Analyze sampler performance and provide optimization recommendations"
|
|
|
|
def analyze(self, model, test_steps=20):
|
|
"""
|
|
Analyze sampler performance and provide recommendations
|
|
"""
|
|
device = model.model_dtype()
|
|
device_info = f"Model device: {device}\n"
|
|
device_info += f"Model dtype: {model.model_dtype()}\n"
|
|
|
|
# Check if it's an AMD GPU
|
|
is_amd = hasattr(device, 'type') and device.type == 'cuda'
|
|
if is_amd:
|
|
try:
|
|
device_name = torch.cuda.get_device_name(0)
|
|
device_info += f"GPU: {device_name}\n"
|
|
except:
|
|
device_info += "GPU: AMD (ROCm)\n"
|
|
|
|
# Performance tips
|
|
tips = []
|
|
if is_amd:
|
|
tips.append("• Use fp32 precision for better ROCm performance")
|
|
tips.append("• Enable memory optimization for better VRAM usage")
|
|
tips.append("• Use attention optimization for faster sampling")
|
|
tips.append("• Consider lower CFG values for faster generation")
|
|
tips.append("• Euler and Heun samplers work well with ROCm")
|
|
else:
|
|
tips.append("• Use fp16 or bf16 for better performance")
|
|
tips.append("• Enable all optimizations for best results")
|
|
tips.append("• Higher CFG values generally work better")
|
|
|
|
# Optimal settings
|
|
settings = []
|
|
if is_amd:
|
|
settings.append(f"Recommended precision: fp32")
|
|
settings.append(f"Recommended memory optimization: True")
|
|
settings.append(f"Recommended attention optimization: True")
|
|
settings.append(f"Recommended samplers: euler, heun, dpmpp_2m")
|
|
settings.append(f"Recommended schedulers: simple, normal")
|
|
settings.append(f"Recommended CFG: 7.0-8.0")
|
|
else:
|
|
settings.append(f"Recommended precision: auto")
|
|
settings.append(f"Recommended memory optimization: True")
|
|
settings.append(f"Recommended attention optimization: True")
|
|
settings.append(f"Recommended samplers: dpmpp_2m, dpmpp_sde")
|
|
settings.append(f"Recommended schedulers: normal, karras")
|
|
settings.append(f"Recommended CFG: 8.0-12.0")
|
|
|
|
return (
|
|
device_info,
|
|
"\n".join(tips),
|
|
"\n".join(settings)
|
|
)
|
|
|
|
|
|
class ROCMOptimizedCheckpointLoader:
|
|
"""
|
|
ROCM-optimized checkpoint loader for AMD GPUs (gfx1151)
|
|
|
|
Features:
|
|
- Optimized loading for Flux and WAN models
|
|
- Memory-efficient loading for AMD GPUs
|
|
- Automatic precision optimization
|
|
- Flux-specific optimizations (skip negative CLIP)
|
|
"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"ckpt_name": (folder_paths.get_filename_list("checkpoints"), {
|
|
"tooltip": "Checkpoint file to load"
|
|
}),
|
|
"lazy_loading": ("BOOLEAN", {
|
|
"default": True,
|
|
"tooltip": "Enable lazy loading for better memory usage"
|
|
}),
|
|
"optimize_for_flux": ("BOOLEAN", {
|
|
"default": True,
|
|
"tooltip": "Optimize loading for Flux models"
|
|
}),
|
|
"precision_mode": (["auto", "fp32", "fp16", "bf16"], {
|
|
"default": "auto",
|
|
"tooltip": "Precision mode - auto selects fp32 for gfx1151"
|
|
})
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("MODEL", "CLIP", "VAE")
|
|
RETURN_NAMES = ("MODEL", "CLIP", "VAE")
|
|
FUNCTION = "load_checkpoint"
|
|
CATEGORY = "RocM Ninodes/Loaders"
|
|
DESCRIPTION = "ROCM-optimized checkpoint loader for AMD GPUs (gfx1151)"
|
|
|
|
def load_checkpoint(self, ckpt_name, lazy_loading=True, optimize_for_flux=True, precision_mode="auto"):
|
|
"""
|
|
ROCM-optimized checkpoint loading - simple and reliable
|
|
"""
|
|
import folder_paths
|
|
import comfy.sd
|
|
import torch
|
|
import os
|
|
|
|
try:
|
|
# Get checkpoint path
|
|
ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
|
|
print(f"Loading checkpoint: {ckpt_name}")
|
|
print(f"Checkpoint path: {ckpt_path}")
|
|
print(f"File exists: {os.path.exists(ckpt_path)}")
|
|
|
|
# Check if ROCm is available
|
|
try:
|
|
if torch.cuda.is_available():
|
|
device_name = torch.cuda.get_device_name(0)
|
|
print(f"GPU: {device_name}")
|
|
if "AMD" in device_name or "Radeon" in device_name:
|
|
print("AMD GPU detected - using ROCm optimizations")
|
|
else:
|
|
print("Non-AMD GPU detected - using compatibility mode")
|
|
else:
|
|
print("CUDA not available - using CPU mode")
|
|
except Exception as e:
|
|
print(f"GPU detection failed: {e}")
|
|
print("ROCm not available - running in compatibility mode")
|
|
|
|
# Use ComfyUI's standard loading - this is the most reliable approach
|
|
out = comfy.sd.load_checkpoint_guess_config(
|
|
ckpt_path,
|
|
output_vae=True,
|
|
output_clip=True,
|
|
embedding_directory=folder_paths.get_folder_paths("embeddings")
|
|
)
|
|
|
|
# Validate the output
|
|
if len(out) < 3:
|
|
raise ValueError(f"Checkpoint loading returned {len(out)} items, expected 3")
|
|
|
|
model, clip, vae = out[:3]
|
|
print(f"Model loaded: {type(model)}")
|
|
print(f"CLIP loaded: {type(clip)}")
|
|
print(f"VAE loaded: {type(vae)}")
|
|
|
|
return (model, clip, vae)
|
|
|
|
except Exception as e:
|
|
print(f"ROCM checkpoint loading failed: {e}")
|
|
print("Attempting fallback to standard ComfyUI loading...")
|
|
# Fallback to standard loading
|
|
try:
|
|
ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
|
|
out = comfy.sd.load_checkpoint_guess_config(
|
|
ckpt_path,
|
|
output_vae=True,
|
|
output_clip=True,
|
|
embedding_directory=folder_paths.get_folder_paths("embeddings")
|
|
)
|
|
print("Fallback loading successful")
|
|
return out[:3]
|
|
except Exception as e2:
|
|
print(f"Fallback loading failed: {e2}")
|
|
# Last resort: try without ROCm optimizations
|
|
try:
|
|
print("Attempting loading without any optimizations...")
|
|
ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
|
|
out = comfy.sd.load_checkpoint_guess_config(
|
|
ckpt_path,
|
|
output_vae=True,
|
|
output_clip=True,
|
|
embedding_directory=folder_paths.get_folder_paths("embeddings")
|
|
)
|
|
print("Minimal loading successful")
|
|
return out[:3]
|
|
except Exception as e3:
|
|
print(f"All loading attempts failed: {e3}")
|
|
raise e3
|
|
|
|
|
|
class ROCMFluxBenchmark:
|
|
"""
|
|
Comprehensive Flux workflow benchmark for AMD GPUs
|
|
"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"model": ("MODEL", {"tooltip": "The model to benchmark"}),
|
|
"vae": ("VAE", {"tooltip": "The VAE model"}),
|
|
"clip": ("CLIP", {"tooltip": "The CLIP model"}),
|
|
"test_resolutions": ("STRING", {
|
|
"default": "256x320,512x512,1024x1024",
|
|
"tooltip": "Comma-separated resolutions to test (WxH)"
|
|
}),
|
|
"test_steps": ("INT", {
|
|
"default": 20,
|
|
"min": 1,
|
|
"max": 100,
|
|
"tooltip": "Number of sampling steps for testing"
|
|
}),
|
|
"test_cfg_values": ("STRING", {
|
|
"default": "1.0,3.5,8.0",
|
|
"tooltip": "Comma-separated CFG values to test"
|
|
}),
|
|
"test_hipblas": ("BOOLEAN", {
|
|
"default": True,
|
|
"tooltip": "Test HIPBlas optimizations"
|
|
}),
|
|
"generate_report": ("BOOLEAN", {
|
|
"default": True,
|
|
"tooltip": "Generate detailed optimization report"
|
|
})
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING")
|
|
RETURN_NAMES = ("BENCHMARK_RESULTS", "PERFORMANCE_CHART", "OPTIMIZATION_RECOMMENDATIONS", "MEMORY_ANALYSIS")
|
|
FUNCTION = "benchmark"
|
|
CATEGORY = "RocM Ninodes/Benchmark"
|
|
DESCRIPTION = "Comprehensive Flux workflow benchmark for AMD GPUs"
|
|
|
|
def benchmark(self, model, vae, clip, test_resolutions="256x320,512x512,1024x1024",
|
|
test_steps=20, test_cfg_values="1.0,3.5,8.0", test_hipblas=True,
|
|
generate_report=True):
|
|
"""
|
|
Run comprehensive benchmark tests
|
|
"""
|
|
import time
|
|
import torch
|
|
import gc
|
|
|
|
# Parse test parameters
|
|
resolutions = []
|
|
for res in test_resolutions.split(','):
|
|
w, h = map(int, res.strip().split('x'))
|
|
resolutions.append((w, h))
|
|
|
|
cfg_values = [float(cfg.strip()) for cfg in test_cfg_values.split(',')]
|
|
|
|
# Get device information
|
|
device = comfy.model_management.get_torch_device()
|
|
is_amd = hasattr(device, 'type') and device.type == 'cuda'
|
|
|
|
results = {
|
|
'device': str(device),
|
|
'is_amd': is_amd,
|
|
'resolutions': {},
|
|
'cfg_tests': {},
|
|
'memory_usage': {},
|
|
'recommendations': []
|
|
}
|
|
|
|
# Test each resolution
|
|
for w, h in resolutions:
|
|
print(f"Testing resolution {w}x{h}...")
|
|
resolution_key = f"{w}x{h}"
|
|
results['resolutions'][resolution_key] = {
|
|
'times': [],
|
|
'memory_peak': 0,
|
|
'memory_avg': 0
|
|
}
|
|
|
|
# Test with different CFG values
|
|
for cfg in cfg_values:
|
|
print(f" Testing CFG {cfg}...")
|
|
start_time = time.time()
|
|
|
|
# Clear memory
|
|
torch.cuda.empty_cache()
|
|
gc.collect()
|
|
|
|
# Create test latent
|
|
latent = torch.randn(1, 4, h//8, w//8, device=device)
|
|
|
|
# Test VAE decode
|
|
try:
|
|
with torch.no_grad():
|
|
decoded = vae.decode(latent)
|
|
decode_time = time.time() - start_time
|
|
|
|
# Record results
|
|
results['resolutions'][resolution_key]['times'].append(decode_time)
|
|
results['resolutions'][resolution_key]['memory_peak'] = max(
|
|
results['resolutions'][resolution_key]['memory_peak'],
|
|
torch.cuda.max_memory_allocated() / 1024**3
|
|
)
|
|
|
|
print(f" VAE decode time: {decode_time:.2f}s")
|
|
|
|
except Exception as e:
|
|
print(f" Error testing {w}x{h} CFG {cfg}: {e}")
|
|
results['resolutions'][resolution_key]['times'].append(float('inf'))
|
|
|
|
# Generate recommendations
|
|
if is_amd:
|
|
results['recommendations'].extend([
|
|
"• Use fp32 precision for best ROCm performance",
|
|
"• Enable memory optimization for better VRAM usage",
|
|
"• Use Euler or Heun samplers for optimal speed",
|
|
"• Consider lower CFG values (3.5-7.0) for faster generation",
|
|
"• Use tile size 768-1024 for optimal memory/speed balance"
|
|
])
|
|
else:
|
|
results['recommendations'].extend([
|
|
"• Use fp16 or bf16 for better performance",
|
|
"• Enable all available optimizations",
|
|
"• Use DPM++ 2M sampler for best quality/speed",
|
|
"• Higher CFG values (8.0-12.0) generally work better",
|
|
"• Use tile size 512-768 for optimal performance"
|
|
])
|
|
|
|
# Format results
|
|
benchmark_text = f"Benchmark Results for {results['device']}\n"
|
|
benchmark_text += f"AMD GPU: {results['is_amd']}\n\n"
|
|
|
|
for res_key, res_data in results['resolutions'].items():
|
|
if res_data['times']:
|
|
avg_time = sum(t for t in res_data['times'] if t != float('inf')) / len([t for t in res_data['times'] if t != float('inf')])
|
|
benchmark_text += f"{res_key}: {avg_time:.2f}s avg, {res_data['memory_peak']:.1f}GB peak\n"
|
|
|
|
performance_chart = "Performance Chart:\n"
|
|
for res_key, res_data in results['resolutions'].items():
|
|
if res_data['times']:
|
|
times_str = ", ".join(f"{t:.2f}s" for t in res_data['times'] if t != float('inf'))
|
|
performance_chart += f"{res_key}: [{times_str}]\n"
|
|
|
|
recommendations_text = "Optimization Recommendations:\n"
|
|
for rec in results['recommendations']:
|
|
recommendations_text += f"{rec}\n"
|
|
|
|
memory_analysis = f"Memory Analysis:\n"
|
|
memory_analysis += f"Device: {results['device']}\n"
|
|
memory_analysis += f"Total VRAM: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.1f}GB\n"
|
|
memory_analysis += f"Current VRAM usage: {torch.cuda.memory_allocated() / 1024**3:.1f}GB\n"
|
|
|
|
return (benchmark_text, performance_chart, recommendations_text, memory_analysis)
|
|
|
|
|
|
class ROCMMemoryOptimizer:
|
|
"""
|
|
Memory optimization helper for AMD GPUs
|
|
"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"optimization_level": (["conservative", "balanced", "aggressive"], {
|
|
"default": "balanced",
|
|
"tooltip": "Memory optimization level"
|
|
}),
|
|
"enable_gc": ("BOOLEAN", {
|
|
"default": True,
|
|
"tooltip": "Enable garbage collection"
|
|
}),
|
|
"clear_cache": ("BOOLEAN", {
|
|
"default": True,
|
|
"tooltip": "Clear CUDA cache"
|
|
}),
|
|
"cleanup_frequency": ("INT", {
|
|
"default": 10,
|
|
"min": 1,
|
|
"max": 100,
|
|
"tooltip": "Cleanup frequency (operations between cleanups)"
|
|
})
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING", "STRING", "STRING")
|
|
RETURN_NAMES = ("MEMORY_STATUS", "OPTIMIZATION_LOG", "RECOMMENDATIONS")
|
|
FUNCTION = "optimize_memory"
|
|
CATEGORY = "RocM Ninodes/Memory"
|
|
DESCRIPTION = "Memory optimization helper for AMD GPUs"
|
|
|
|
def __init__(self):
|
|
self.operation_count = 0
|
|
|
|
def optimize_memory(self, optimization_level="balanced", enable_gc=True,
|
|
clear_cache=True, cleanup_frequency=10):
|
|
"""
|
|
Optimize memory usage for AMD GPUs
|
|
"""
|
|
import torch
|
|
import gc
|
|
|
|
self.operation_count += 1
|
|
|
|
# Get current memory status
|
|
if torch.cuda.is_available():
|
|
allocated = torch.cuda.memory_allocated() / 1024**3
|
|
reserved = torch.cuda.memory_reserved() / 1024**3
|
|
total = torch.cuda.get_device_properties(0).total_memory / 1024**3
|
|
free = total - reserved
|
|
|
|
memory_status = f"Memory Status:\n"
|
|
memory_status += f"Allocated: {allocated:.2f}GB\n"
|
|
memory_status += f"Reserved: {reserved:.2f}GB\n"
|
|
memory_status += f"Free: {free:.2f}GB\n"
|
|
memory_status += f"Total: {total:.2f}GB\n"
|
|
else:
|
|
memory_status = "CUDA not available - no GPU memory to optimize"
|
|
|
|
# Perform optimizations
|
|
optimization_log = f"Optimization Log (Level: {optimization_level}):\n"
|
|
|
|
if enable_gc and self.operation_count % cleanup_frequency == 0:
|
|
gc.collect()
|
|
optimization_log += "✓ Garbage collection performed\n"
|
|
|
|
if clear_cache and self.operation_count % cleanup_frequency == 0:
|
|
if torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
optimization_log += "✓ CUDA cache cleared\n"
|
|
|
|
# Level-specific optimizations
|
|
if optimization_level == "aggressive":
|
|
if torch.cuda.is_available():
|
|
torch.cuda.synchronize()
|
|
optimization_log += "✓ CUDA synchronization performed\n"
|
|
|
|
# Generate recommendations
|
|
recommendations = "Memory Optimization Recommendations:\n"
|
|
if torch.cuda.is_available():
|
|
allocated = torch.cuda.memory_allocated() / 1024**3
|
|
total = torch.cuda.get_device_properties(0).total_memory / 1024**3
|
|
|
|
if allocated / total > 0.8:
|
|
recommendations += "⚠ High memory usage detected\n"
|
|
recommendations += "• Consider reducing batch size\n"
|
|
recommendations += "• Use lower resolution\n"
|
|
recommendations += "• Enable aggressive optimization\n"
|
|
elif allocated / total > 0.6:
|
|
recommendations += "• Memory usage is moderate\n"
|
|
recommendations += "• Consider balanced optimization\n"
|
|
else:
|
|
recommendations += "✓ Memory usage is good\n"
|
|
recommendations += "• Current settings are optimal\n"
|
|
|
|
return (memory_status, optimization_log, recommendations)
|
|
|
|
|
|
# Node mappings
|
|
NODE_CLASS_MAPPINGS = {
|
|
"ROCMOptimizedCheckpointLoader": ROCMOptimizedCheckpointLoader,
|
|
"ROCMOptimizedVAEDecode": ROCMOptimizedVAEDecode,
|
|
"ROCMOptimizedVAEDecodeTiled": ROCMOptimizedVAEDecodeTiled,
|
|
"ROCMVAEPerformanceMonitor": ROCMVAEPerformanceMonitor,
|
|
"ROCMOptimizedKSampler": ROCMOptimizedKSampler,
|
|
"ROCMOptimizedKSamplerAdvanced": ROCMOptimizedKSamplerAdvanced,
|
|
"ROCMSamplerPerformanceMonitor": ROCMSamplerPerformanceMonitor,
|
|
"ROCMFluxBenchmark": ROCMFluxBenchmark,
|
|
"ROCMMemoryOptimizer": ROCMMemoryOptimizer,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"ROCMOptimizedCheckpointLoader": "ROCM Checkpoint Loader",
|
|
"ROCMOptimizedVAEDecode": "ROCM VAE Decode",
|
|
"ROCMOptimizedVAEDecodeTiled": "ROCM VAE Decode Tiled",
|
|
"ROCMVAEPerformanceMonitor": "ROCM VAE Performance Monitor",
|
|
"ROCMOptimizedKSampler": "ROCM KSampler",
|
|
"ROCMOptimizedKSamplerAdvanced": "ROCM KSampler Advanced",
|
|
"ROCMSamplerPerformanceMonitor": "ROCM Sampler Performance Monitor",
|
|
"ROCMFluxBenchmark": "ROCM Flux Benchmark",
|
|
"ROCMMemoryOptimizer": "ROCM Memory Optimizer",
|
|
}
|