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feat: support the wan22's dit and tp parallel. #1445

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feat: support the wan22's dit and tp parallel. #1445

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729553c
feat: support the dit and tp for wan2.2.
ethan686 May 13, 2026
bd29368
feat: add the model args for all wan2.2.
ethan686 May 14, 2026
45ed8b7
fix: resolve the issue found by gemini.
ethan686 May 14, 2026
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56 changes: 56 additions & 0 deletions xllm/core/framework/model/model_args.h
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Original file line number Diff line number Diff line change
Expand Up @@ -404,12 +404,43 @@ struct ModelArgs {
PROPERTY(bool, use_quant_conv) = false;
PROPERTY(bool, use_post_quant_conv) = false;

// Wan_2.2_ VAE related args
PROPERTY(int64_t, vae_in_channels) = -1;
PROPERTY(int64_t, vae_out_channels) = -1;
PROPERTY(int64_t, vae_base_dim) = 0;
PROPERTY(int64_t, vae_z_dim) = 0;
PROPERTY(int64_t, vae_scale_factor_temporal) = 0;
PROPERTY(int64_t, vae_scale_factor_spatial) = 0;
PROPERTY(std::vector<int64_t>, vae_dim_mult) = {};
PROPERTY(int64_t, vae_num_res_blocks) = 0;
PROPERTY(std::vector<double>, vae_attn_scales) = {};
PROPERTY(std::vector<bool>, vae_temporal_downsample) = {};
PROPERTY(std::vector<double>, vae_latents_mean) = {};
PROPERTY(std::vector<double>, vae_latents_std) = {};
PROPERTY(double, vae_dropout) = 0.0;
PROPERTY(bool, vae_is_residual) = false;

// dit related args
PROPERTY(int64_t, joint_attention_dim) = 0;
PROPERTY(int64_t, pooled_projection_dim) = 0;
PROPERTY(bool, guidance_embeds) = true;
PROPERTY(std::vector<int64_t>, axes_dims_rope) = {};
PROPERTY(int64_t, num_single_layers) = 0;
PROPERTY(int, timestep_guidance_channels) = 256;
PROPERTY(double, eps) = 1e-6;
PROPERTY(int64_t, patch_size) = 1;
PROPERTY(std::vector<int64_t>, wan_patch_size) = { 1, 2, 2 };
PROPERTY(bool, cross_attn_norm) = true;
PROPERTY(int64_t, ffn_dim) = 13824;
PROPERTY(int64_t, time_freq_dim) = 256;
PROPERTY(int64_t, dit_in_channels) = 36;
PROPERTY(int64_t, dit_out_channels) = 16;
PROPERTY(std::string, qk_norm) = "rms_norm_across_heads";
PROPERTY(int64_t, rope_max_seq_len) = 1024;
PROPERTY(int64_t, text_embed_dim) = 4096;
PROPERTY(int64_t, image_embed_dim) = -1;
PROPERTY(int64_t, added_kv_proj_dim) = -1;
PROPERTY(int64_t, pos_embed_seq_len) = -1;

// t5 related args
PROPERTY(int64_t, d_model) = 0;
Expand All @@ -430,6 +461,31 @@ struct ModelArgs {
PROPERTY(int64_t, base_image_seq_len) = 0;
PROPERTY(int64_t, max_image_seq_len) = 0;
PROPERTY(float, shift_terminal) = 0;
PROPERTY(float, beta_start) = 0.0001f;
PROPERTY(float, beta_end) = 0.02f;
PROPERTY(std::string, beta_schedule) = "linear";
PROPERTY(std::vector<float>, trained_betas) = {};
PROPERTY(int64_t, solver_order) = 2;
PROPERTY(std::string, prediction_type) = "flow_prediction";
PROPERTY(bool, thresholding) = false;
PROPERTY(float, dynamic_thresholding_ratio) = 0.995f;
PROPERTY(float, sample_max_value) = 1.0f;
PROPERTY(bool, predict_x0) = true;
PROPERTY(std::string, solver_type) = "bh2";
PROPERTY(bool, lower_order_final) = true;
PROPERTY(std::vector<int64_t>, disable_corrector) = {};
PROPERTY(bool, use_karras_sigmas) = false;
PROPERTY(bool, use_exponential_sigmas) = false;
PROPERTY(bool, use_beta_sigmas) = false;
PROPERTY(bool, use_flow_sigmas) = true;
PROPERTY(float, flow_shift) = 3.0f;
PROPERTY(std::string, timestep_spacing) = "linspace";
PROPERTY(int64_t, steps_offset) = 0;
PROPERTY(std::string, final_sigmas_type) = "zero";
PROPERTY(bool, rescale_betas_zero_snr) = false;
PROPERTY(std::string, time_shift_type) = "exponential";
PROPERTY(float, sigma_min) = 0.0f;
PROPERTY(float, sigma_max) = 0.0f;

// qwen_image_edit_2509 vae related args
PROPERTY(int64_t, base_dim) = 0;
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