v2.2.0: clean up ROCM SamplerCustomAdvanced interface
- Remove optimize_for_video (auto-detects 5D video latents) - Remove precision_mode (was a no-op) - compatibility_mode moved to advanced=True toggle - Node now matches stock interface exactly (5 inputs) with only an advanced debug toggle added - Video workflow auto-detected from latent shape (5D, T > 1) - Architecture logging explicitly shows GPU family on ROCm - Benchmark node also cleaned up (no extra params)
This commit is contained in:
iGavroche
@@ -39,9 +39,7 @@ This is the standard pattern for LTX Video, LTX 2.3 AV, and many other advanced
|
||||
| `sampler` | SAMPLER | Yes | e.g. KSamplerSelect |
|
||||
| `sigmas` | SIGMAS | Yes | e.g. BasicScheduler |
|
||||
| `latent_image` | LATENT | Yes | e.g. LTXVConcatAVLatent |
|
||||
| `optimize_for_video` | BOOLEAN | No (default: False) | Disables previews when latent is 5D with T > 1 |
|
||||
| `precision_mode` | COMBO | No (default: auto) | `auto`, `fp32`, `bf16` — ROCm precision hint |
|
||||
| `compatibility_mode` | BOOLEAN | No (default: False) | When True, skips all ROCm optimizations (pure stock behavior) |
|
||||
| `compatibility_mode` | BOOLEAN | No (advanced) | Skip all ROCm optimizations, use pure stock behavior |
|
||||
|
||||
## Outputs
|
||||
|
||||
@@ -57,18 +55,21 @@ This is the standard pattern for LTX Video, LTX 2.3 AV, and many other advanced
|
||||
1. Delete the stock `SamplerCustomAdvanced` node
|
||||
2. Add `ROCM SamplerCustomAdvanced` from `ROCm Ninodes/Sampling`
|
||||
3. Wire the same connections (noise, guider, sampler, sigmas, latent_image)
|
||||
4. Optionally configure `optimize_for_video`, `precision_mode`, or `compatibility_mode`
|
||||
5. Run — the console will show per-step progress, model detection, and completion
|
||||
4. Run — the console will show GPU architecture, model detection, and per-step progress
|
||||
|
||||
### LTX 2.3 / LTX Video Workflows
|
||||
|
||||
For LTX-based workflows (like `LTX2.3-Director-App-Mariah.json`), the `latent_image` input typically comes from `LTXVConcatAVLatent`. ROCMSamplerCustomAdvanced detects the flow-matching model type (128ch latent, memory factor 5.5x) and applies appropriate memory management before and after sampling.
|
||||
For LTX-based workflows (like `LTX2.3-Director-App-Mariah.json`), the `latent_image` input typically comes from `LTXVConcatAVLatent`. ROCMSamplerCustomAdvanced detects the flow-matching model type (128ch latent, memory factor 5.5x) and applies appropriate memory management before and after sampling. Video (5D latent with T > 1) is auto-detected — previews are disabled for multi-frame latents to reduce overhead.
|
||||
|
||||
### When to Use `compatibility_mode`
|
||||
|
||||
Enable it via the node's advanced properties panel if you suspect the ROCm optimizations are causing issues. This returns the exact stock behavior for debugging.
|
||||
|
||||
## Benchmark Node
|
||||
|
||||
`ROCMSamplerCustomAdvancedBenchmark` (`ROCm Ninodes/Sampling > ROCm SamplerCustomAdvanced Benchmark`) runs both the stock and ROCm-optimized sampler in sequence on identical inputs and outputs a comparison string.
|
||||
|
||||
**Inputs:** Same as SamplerCustomAdvanced.
|
||||
**Inputs:** Same as SamplerCustomAdvanced (no extra params).
|
||||
|
||||
**Outputs:**
|
||||
- `LATENT` — result from the ROCm-optimized run
|
||||
|
||||
+25
-45
@@ -449,8 +449,8 @@ class ROCMSamplerCustomAdvanced(io.ComfyNode):
|
||||
Key enhancements over stock SamplerCustomAdvanced:
|
||||
- Auto-detects AMD GPU architecture and applies optimal backend settings
|
||||
- Emergency memory defrag for high-memory models (128ch latents)
|
||||
- Optional BF16 precision hint for supported AMD GPUs
|
||||
- Enhanced callback with ETA, per-step timing, and video-workflow optimization
|
||||
- Enhanced callback with ETA and per-step timing
|
||||
- Video workflow auto-detection (5D latent → no previews)
|
||||
- Gentle post-sample memory cleanup
|
||||
"""
|
||||
|
||||
@@ -458,7 +458,7 @@ class ROCMSamplerCustomAdvanced(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="ROCMSamplerCustomAdvanced",
|
||||
display_name="ROCM SamplerCustomAdvanced",
|
||||
display_name="ROCm SamplerCustomAdvanced",
|
||||
category="ROCm Ninodes/Sampling",
|
||||
description="Drop-in replacement for SamplerCustomAdvanced with ROCm memory management, backend tuning, and detailed progress tracking. Particularly beneficial for high-memory models like LTX Video (128ch latent).",
|
||||
inputs=[
|
||||
@@ -467,13 +467,8 @@ class ROCMSamplerCustomAdvanced(io.ComfyNode):
|
||||
io.Sampler.Input("sampler"),
|
||||
io.Sigmas.Input("sigmas"),
|
||||
io.Latent.Input("latent_image"),
|
||||
io.Boolean.Input("optimize_for_video", optional=True, default=False,
|
||||
tooltip="Disable previews for multi-frame latents (video workflows)"),
|
||||
io.Combo.Input("precision_mode", optional=True, default="auto",
|
||||
options=["auto", "fp32", "bf16"],
|
||||
tooltip="ROCm precision hint (no-op on CUDA/CPU)"),
|
||||
io.Boolean.Input("compatibility_mode", optional=True, default=False,
|
||||
tooltip="Force pure stock behavior, no ROCm optimizations"),
|
||||
io.Boolean.Input("compatibility_mode", optional=True, default=False, advanced=True,
|
||||
tooltip="Skip all ROCm optimizations, force pure stock behavior"),
|
||||
],
|
||||
outputs=[
|
||||
io.Latent.Output(display_name="output"),
|
||||
@@ -483,31 +478,28 @@ class ROCMSamplerCustomAdvanced(io.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
def execute(cls, noise, guider, sampler, sigmas, latent_image,
|
||||
optimize_for_video=False, precision_mode="auto", compatibility_mode=False) -> io.NodeOutput:
|
||||
# ── Detect architecture and model type ──────────────────────────
|
||||
compatibility_mode=False) -> io.NodeOutput:
|
||||
model = guider.model_patcher
|
||||
|
||||
# ── ROCm setup (skipped in compatibility_mode) ──────────────────
|
||||
if not compatibility_mode:
|
||||
arch_info = detect_architecture()
|
||||
model_info = detect_model_sampling_type(model)
|
||||
apply_rocm_backend_settings(arch_info)
|
||||
|
||||
is_rocm = bool(getattr(torch.version, 'hip', None))
|
||||
if is_rocm:
|
||||
print(f"🚀 ROCm detected on {arch_info['family']} "
|
||||
f"({arch_info.get('arch_name', 'unknown')}) "
|
||||
f"— applying backend tuning", flush=True)
|
||||
|
||||
if model_info["has_high_memory"]:
|
||||
print(f"💾 High-memory model detected (factor: {model_info['memory_usage_factor']}) — cleaning up")
|
||||
print(f"💾 High-memory model (factor: {model_info['memory_usage_factor']}x, "
|
||||
f"{model_info['latent_channels']}ch latent) — defragging VRAM", flush=True)
|
||||
emergency_memory_cleanup()
|
||||
|
||||
if model_info["model_type"] == "flow":
|
||||
print(f"🌊 Flow-matching model detected ({model_info['latent_channels']}ch latent)")
|
||||
elif model_info["is_pixel_space"]:
|
||||
print(f"📷 Pixel-space model detected — no VAE decode needed downstream")
|
||||
|
||||
is_rocm = bool(getattr(torch.version, 'hip', None))
|
||||
if is_rocm and torch.cuda.is_available() and precision_mode == "bf16":
|
||||
try:
|
||||
if getattr(torch.cuda, 'is_bf16_supported', lambda: False)():
|
||||
pass
|
||||
except Exception:
|
||||
pass
|
||||
print(f"🌊 Flow-matching model ({model_info['latent_channels']}ch)", flush=True)
|
||||
|
||||
# ── Stock SamplerCustomAdvanced logic ──────────────────────────
|
||||
latent = latent_image
|
||||
@@ -527,14 +519,12 @@ class ROCMSamplerCustomAdvanced(io.ComfyNode):
|
||||
x0_output = {}
|
||||
total_steps = sigmas.shape[-1] - 1
|
||||
|
||||
# ── Video optimization ──────────────────────────────────────────
|
||||
is_video_workflow = False
|
||||
if not compatibility_mode and optimize_for_video and latent_image_tensor.ndim == 5 and latent_image_tensor.shape[2] > 1:
|
||||
is_video_workflow = True
|
||||
# ── Auto-detect video (5D latent with T > 1) ──────────────────────
|
||||
is_video = latent_image_tensor.ndim == 5 and latent_image_tensor.shape[2] > 1
|
||||
|
||||
# ── Create previewer once (like stock prepare_callback) ──────
|
||||
# ── Create previewer once (like stock prepare_callback) ──────────
|
||||
previewer = None
|
||||
if not is_video_workflow:
|
||||
if not is_video:
|
||||
try:
|
||||
previewer = latent_preview.get_previewer(
|
||||
guider.model_patcher.load_device,
|
||||
@@ -556,7 +546,6 @@ class ROCMSamplerCustomAdvanced(io.ComfyNode):
|
||||
progress_pct = ((step + 1) / total_steps) * 100
|
||||
pct_key = (int(progress_pct) // 10) * 10
|
||||
|
||||
# ── Update progress bar with preview (throttled) ────────
|
||||
if current_time - last_update_time[0] >= 0.3 or step == total_steps - 1:
|
||||
preview_bytes = None
|
||||
if previewer and step % 5 == 0:
|
||||
@@ -570,11 +559,9 @@ class ROCMSamplerCustomAdvanced(io.ComfyNode):
|
||||
pass
|
||||
last_update_time[0] = current_time
|
||||
|
||||
# ── Console timing at 10% milestones only ──────────────
|
||||
if step == 0 or step == total_steps - 1 or pct_key not in reported_milestones:
|
||||
reported_milestones.add(pct_key)
|
||||
elapsed = current_time - start_time
|
||||
wf_type = "Video" if is_video_workflow else "Image"
|
||||
if step == 0:
|
||||
print(f"📊 ROCm CustomAdvanced: Starting {total_steps} steps...", flush=True)
|
||||
else:
|
||||
@@ -587,11 +574,11 @@ class ROCMSamplerCustomAdvanced(io.ComfyNode):
|
||||
f"({avg:.2f}s/step)", flush=True)
|
||||
|
||||
callback = enhanced_callback
|
||||
disable_pbar = is_video_workflow
|
||||
disable_pbar = is_video
|
||||
|
||||
# ── Sampling ────────────────────────────────────────────────────
|
||||
print(f"🚀 ROCm SamplerCustomAdvanced: {total_steps} steps"
|
||||
f"{' (video mode)' if is_video_workflow else ''}", flush=True)
|
||||
f"{' (video mode)' if is_video else ''}", flush=True)
|
||||
samples = guider.sample(
|
||||
noise.generate_noise(latent), latent_image_tensor, sampler, sigmas,
|
||||
denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar,
|
||||
@@ -649,10 +636,6 @@ class ROCMSamplerCustomAdvancedBenchmark:
|
||||
"sampler": ("SAMPLER",),
|
||||
"sigmas": ("SIGMAS",),
|
||||
"latent_image": ("LATENT",),
|
||||
},
|
||||
"optional": {
|
||||
"optimize_for_video": ("BOOLEAN", {"default": False}),
|
||||
"precision_mode": (["auto", "fp32", "bf16"], {"default": "auto"}),
|
||||
}
|
||||
}
|
||||
|
||||
@@ -662,8 +645,7 @@ class ROCMSamplerCustomAdvancedBenchmark:
|
||||
CATEGORY = "ROCm Ninodes/Sampling"
|
||||
DESCRIPTION = "Compare stock vs ROCm-optimized custom sampler performance"
|
||||
|
||||
def run(self, noise, guider, sampler, sigmas, latent_image,
|
||||
optimize_for_video=False, precision_mode="auto"):
|
||||
def run(self, noise, guider, sampler, sigmas, latent_image):
|
||||
import time as time_module
|
||||
import gc
|
||||
|
||||
@@ -713,9 +695,7 @@ class ROCMSamplerCustomAdvancedBenchmark:
|
||||
if model_info["has_high_memory"]:
|
||||
emergency_memory_cleanup()
|
||||
|
||||
is_video_workflow = False
|
||||
if optimize_for_video and latent_image_tensor.ndim == 5 and latent_image_tensor.shape[2] > 1:
|
||||
is_video_workflow = True
|
||||
is_video = latent_image_tensor.ndim == 5 and latent_image_tensor.shape[2] > 1
|
||||
|
||||
mem_before_rocm = torch.cuda.memory_allocated(0) if torch.cuda.is_available() else 0
|
||||
|
||||
@@ -739,7 +719,7 @@ class ROCMSamplerCustomAdvancedBenchmark:
|
||||
rocm_samples = guider.sample(
|
||||
noise.generate_noise(latent), latent_image_tensor, sampler, sigmas,
|
||||
denoise_mask=noise_mask, callback=rocm_callback,
|
||||
disable_pbar=is_video_workflow, seed=noise.seed
|
||||
disable_pbar=is_video, seed=noise.seed
|
||||
)
|
||||
rocm_samples = rocm_samples.to(comfy.model_management.intermediate_device())
|
||||
t3 = time_module.time()
|
||||
|
||||
Reference in New Issue
Block a user