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Isolate sampler replay from text capture

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This commit is contained in:
Daniel Maddern 2026-08-13 15:16:48 +07:00
parent 416a7d9697
commit 889e76b85b
1 changed files with 2 additions and 3 deletions
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5
tools/compare_fl2va_steps.py
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@ -7,9 +7,7 @@ import torch
from h3_blackwell_runtime.checkpoint import H3Checkpoint from h3_blackwell_runtime.checkpoint import H3Checkpoint
from h3_blackwell_runtime.denoiser import H3PackedDenoiser from h3_blackwell_runtime.denoiser import H3PackedDenoiser
from h3_blackwell_runtime.packing import H3PromptPacker, unpatchify_video from h3_blackwell_runtime.packing import H3PromptPacker, unpatchify_video
from h3_blackwell_runtime.qwen3vl_text import Qwen3VLPromptConditioner
from h3_blackwell_runtime.sampler import _audio_sigma, _unpack_audio, res_multistep_update from h3_blackwell_runtime.sampler import _audio_sigma, _unpack_audio, res_multistep_update
from h3_blackwell_runtime.token_refiner import H3TokenRefiner
root = "/artifacts/fl2va-sampler-reference" root = "/artifacts/fl2va-sampler-reference"
@ -17,7 +15,8 @@ initial = torch.load(f"{root}/initial.pt", map_location="cuda", weights_only=Fal
steps = [torch.load(path, map_location="cuda", weights_only=False) for path in sorted(glob.glob(f"{root}/step_*.pt"))] steps = [torch.load(path, map_location="cuda", weights_only=False) for path in sorted(glob.glob(f"{root}/step_*.pt"))]
sigmas = initial["sigmas"].to("cuda") sigmas = initial["sigmas"].to("cuda")
checkpoint = H3Checkpoint("/models/minimax_h3_fl2va_pruned_nvfp4.safetensors") checkpoint = H3Checkpoint("/models/minimax_h3_fl2va_pruned_nvfp4.safetensors")
text = H3TokenRefiner(checkpoint)(Qwen3VLPromptConditioner("/text-encoders/qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors")("A brass-and-paper dragon flies above a rain-washed old city at blue hour.")) captured_input = torch.load("/artifacts/capture/input.pt", map_location="cuda", weights_only=False)
text = captured_input["hidden"][:17].unsqueeze(0)
model = H3PackedDenoiser.from_checkpoint(checkpoint, attention_backend="sage2").eval() model = H3PackedDenoiser.from_checkpoint(checkpoint, attention_backend="sage2").eval()
packer = H3PromptPacker(checkpoint) packer = H3PromptPacker(checkpoint)