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54a8d721c1
* Expose n_recycle as inference sampler parameter and improve docs The n_recycle parameter was previously hardcoded in the diffusion module config and not overridable at inference time. This exposes it through the inference sampler so users can control recycling iterations via CLI (e.g. inference_sampler.n_recycle=3). Also adds num_timesteps and n_recycle to the "Other CLI Options" docs section, and makes the InputSpecification reference more prominent in the README. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
69 lines
2.2 KiB
YAML
69 lines
2.2 KiB
YAML
# @package _global_
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defaults:
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- base
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- _self_
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_target_: rfd3.engine.RFD3InferenceEngine
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out_dir: ???
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inputs: ??? # null, json, pdb or
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ckpt_path: rfd3
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json_keys_subset: null
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skip_existing: True
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#########################################################
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# Design spec args: overrides args from input json
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specification: {}
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#########################################################
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# Diffusion args
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diffusion_batch_size: 8
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n_batches: 1
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# Inference sampler args | set to None to use the default in the checkpoint's config
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inference_sampler:
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kind: "default" # "default" or "symmetry" to choose the sampler
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# Classifier-free guidance args:
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cfg_features: # set to 0 in the reference CFG step
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- active_donor
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- active_acceptor
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- ref_atomwise_rasa
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use_classifier_free_guidance: False
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cfg_t_max: null # max t to apply cfg guidance
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cfg_scale: 1.5
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center_option: "all" # Options are ["all", "motif", "diffuse"]
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s_trans: 1.0 # Translational noise scale for augmentation during inference
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inference_noise_scaling_factor: 1.0
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allow_realignment: False
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# Recycling
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n_recycle: null # Override model default n_recycle for inference (default from checkpoint: 2)
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# Diffusion args:
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num_timesteps: 200
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step_scale: 1.5 # 1.5 - 1.0 | Higher values lead to less diverse, more designable, structures
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noise_scale: 1.003
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p: 7
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gamma_0: 0.6 # Previously 1.0 | 0.0 for ODE sampling
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gamma_min: 1.0
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s_jitter_origin: 0.0 # Sigma of gaussian noise to jitter the motif offset (equivalent to ORI token Jitter)
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# Saving args
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cleanup_guideposts: True
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cleanup_virtual_atoms: True
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read_sequence_from_sequence_head: True
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output_full_json: True
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# Prefix to add to all output samples
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# Default: None -> f'{jsonfilebasename}_{jsonkey}_{batch}_{model}'
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# Otherwise: string -> f'{string}{jsonkey}_{batch}_{model}'
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# e.g. Empty string -> f'{jsonkey}_{batch}_{model}'
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# e.g. Chunk string -> f'{chunkprefix_}{jsonkey}_{batch}_{model}' (pipelines usage)
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global_prefix: null
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dump_prediction_metadata_json: True
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dump_trajectories: False
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align_trajectory_structures: False
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prevalidate_inputs: False
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low_memory_mode: False # False for standard mode, True for memory efficient tokenization mode
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