llama.cpp verification source 2026-05-22
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examples/diffusion/diffusion.h
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57
examples/diffusion/diffusion.h
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#pragma once
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#include "llama.h"
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#include <cstdint>
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enum diffusion_algorithm {
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DIFFUSION_ALGORITHM_ORIGIN = 0,
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DIFFUSION_ALGORITHM_ENTROPY_BASED = 1,
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DIFFUSION_ALGORITHM_MARGIN_BASED = 2,
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DIFFUSION_ALGORITHM_RANDOM = 3,
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DIFFUSION_ALGORITHM_CONFIDENCE_BASED = 4,
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};
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// Unified transfer scheduling methods
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enum diffusion_transfer_schedule {
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DIFFUSION_TRANSFER_SCHEDULE_TIMESTEP_BASED = 0, // Dream-style: (1.0 - s/t) * remaining
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DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED = 1, // LLaDA-style: process in blocks with get_num_transfer_tokens
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};
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typedef bool (*diffusion_step_callback_t)(int32_t step,
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int32_t total_steps,
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const llama_token * tokens,
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int32_t n_tokens,
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void * user_data);
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struct diffusion_params {
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int32_t steps = 0;
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float temperature = 0;
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llama_token mask_token_id = LLAMA_TOKEN_NULL;
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diffusion_step_callback_t step_callback = nullptr;
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void * step_callback_user_data = nullptr;
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int32_t seed = 0;
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bool visual_mode = false;
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bool shift_logits = false; // Shift logits by -1 after decode
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float top_p = 0.;
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int32_t top_k = 0.;
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diffusion_algorithm algorithm = DIFFUSION_ALGORITHM_CONFIDENCE_BASED;
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diffusion_transfer_schedule schedule = DIFFUSION_TRANSFER_SCHEDULE_TIMESTEP_BASED;
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float cfg_scale = 0.; // Config scale for classifier-free guidance
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float eps = 0.; // Timestep scheduling
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int32_t block_length = 0; // Block size (for block scheduling)
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float alg_temp = 0; // algorithm temperature (0.0 = deterministic)
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bool add_gumbel_noise = false; // Add gumbel noise to the logits if temp > 0.0
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int32_t max_length = 0; // Maximum sequence length
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};
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void diffusion_generate(llama_context * ctx,
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const llama_token * input_tokens,
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llama_token * output_tokens,
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int32_t n_input,
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const diffusion_params & params,
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int32_t & n_generated);
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