Add LightX2V + Wan2.2-TI2V-5B-Turbo GGUF experiment

Benchmarks the dense 5B Turbo model (Q8_0 GGUF + fp8 T5) as a
lower-VRAM alternative to the 14B MoE pipeline. Includes dtype
patches for dense WanModel, Wan 2.2 VAE config (48 channels, 16x
spatial), and Blackwell fp8 workaround.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

experimental/lightx2v_5b/config.json

@@ -0,0 +1,40 @@
+{
+  "_comment": "LightX2V config for Wan2.2-TI2V-5B-Turbo (dense, GGUF). Single DIT checkpoint (not MoE). dit_quantized_ckpt is filled in at runtime by setup_model.py / test_i2v.py.",
+
+  "infer_steps": 4,
+  "target_video_length": 81,
+  "text_len": 512,
+
+  "resize_mode": "adaptive",
+  "resolution": "480p",
+  "target_height": 480,
+  "target_width": 480,
+  "fps": 16,
+
+  "vae_stride": [4, 16, 16],
+  "num_channels_latents": 48,
+
+  "self_attn_1_type": "torch_sdpa",
+  "cross_attn_1_type": "torch_sdpa",
+  "cross_attn_2_type": "torch_sdpa",
+  "modulate_type": "torch",
+  "rope_type": "torch",
+
+  "sample_guide_scale": 1.0,
+  "sample_shift": 5.0,
+  "enable_cfg": false,
+
+  "cpu_offload": false,
+  "offload_granularity": "model",
+  "t5_cpu_offload": true,
+  "vae_cpu_offload": false,
+
+  "use_image_encoder": false,
+
+  "denoising_step_list": [1000, 750, 500, 250],
+
+  "dit_quantized": true,
+  "dit_quant_scheme": "gguf-Q8_0",
+  "t5_quantized": true,
+  "t5_quant_scheme": "fp8-sgl"
+}