Changing number of designs for examples from 10 to 2 to speed up tests

woodsh17 · woodsh17 · commit f43a81d59d69 · 2025-10-10T13:53:59.000-05:00
diff --git a/examples/design_cyclic_oligos.sh b/examples/design_cyclic_oligos.sh
@@ -7,4 +7,4 @@
 # We decay this potential with quadratic form, so that it is applied more strongly initially
 # We specify a total length of 480aa, so each chain is 80 residues long
 
-python ../scripts/run_inference.py --config-name=symmetry inference.symmetry="C6" inference.num_designs=10 inference.output_prefix="example_outputs/C6_oligo" 'potentials.guiding_potentials=["type:olig_contacts,weight_intra:1,weight_inter:0.1"]' potentials.olig_intra_all=True potentials.olig_inter_all=True potentials.guide_scale=2.0 potentials.guide_decay="quadratic" 'contigmap.contigs=[480-480]'
+python ../scripts/run_inference.py --config-name=symmetry inference.symmetry="C6" inference.num_designs=2 inference.output_prefix="example_outputs/C6_oligo" 'potentials.guiding_potentials=["type:olig_contacts,weight_intra:1,weight_inter:0.1"]' potentials.olig_intra_all=True potentials.olig_inter_all=True potentials.guide_scale=2.0 potentials.guide_decay="quadratic" 'contigmap.contigs=[480-480]'
diff --git a/examples/design_dihedral_oligos.sh b/examples/design_dihedral_oligos.sh
@@ -7,4 +7,4 @@
 # We decay this potential with quadratic form, so that it is applied more strongly initially
 # We specify a total length of 320aa, so each chain is 80 residues long
 
-python ../scripts/run_inference.py --config-name=symmetry inference.symmetry="D2" inference.num_designs=10 inference.output_prefix="example_outputs/D2_oligo" 'potentials.guiding_potentials=["type:olig_contacts,weight_intra:1,weight_inter:0.1"]' potentials.olig_intra_all=True potentials.olig_inter_all=True potentials.guide_scale=2.0 potentials.guide_decay="quadratic" 'contigmap.contigs=[320-320]'
+python ../scripts/run_inference.py --config-name=symmetry inference.symmetry="D2" inference.num_designs=2 inference.output_prefix="example_outputs/D2_oligo" 'potentials.guiding_potentials=["type:olig_contacts,weight_intra:1,weight_inter:0.1"]' potentials.olig_intra_all=True potentials.olig_inter_all=True potentials.guide_scale=2.0 potentials.guide_decay="quadratic" 'contigmap.contigs=[320-320]'
diff --git a/examples/design_macrocyclic_binder.sh b/examples/design_macrocyclic_binder.sh
@@ -7,7 +7,7 @@
 ../scripts/run_inference.py \
 --config-name base \
 inference.output_prefix=example_outputs/diffused_binder_cyclic2 \
-inference.num_designs=10 \
+inference.num_designs=2 \
 'contigmap.contigs=[12-18 A3-117/0]' \
 inference.input_pdb=/input_pdbs/7zkr_GABARAP.pdb \
 inference.cyclic=True \
diff --git a/examples/design_macrocyclic_monomer.sh b/examples/design_macrocyclic_monomer.sh
@@ -7,7 +7,7 @@
 ../scripts/run_inference.py \
 --config-name base \
 inference.output_prefix=example_outputs/uncond_cycpep \
-inference.num_designs=10 \
+inference.num_designs=2 \
 'contigmap.contigs=[12-18]' \
 inference.input_pdb=input_pdbs/7zkr_GABARAP.pdb \
 inference.cyclic=True \
diff --git a/examples/design_motifscaffolding.sh b/examples/design_motifscaffolding.sh
@@ -8,4 +8,4 @@
 #   - 10-40 residues (randomly sampled)
 # We generate 10 designs
 
-../scripts/run_inference.py inference.output_prefix=example_outputs/design_motifscaffolding inference.input_pdb=input_pdbs/5TPN.pdb 'contigmap.contigs=[10-40/A163-181/10-40]' inference.num_designs=10
+../scripts/run_inference.py inference.output_prefix=example_outputs/design_motifscaffolding inference.input_pdb=input_pdbs/5TPN.pdb 'contigmap.contigs=[10-40/A163-181/10-40]' inference.num_designs=2
diff --git a/examples/design_motifscaffolding_inpaintseq.sh b/examples/design_motifscaffolding_inpaintseq.sh
@@ -12,4 +12,4 @@
 # We generate 10 designs
 # We then specify that residues 163-168 (inclusive), 170-171 (inclusive) and 179 (inclusive) on the A chain of the input, should be masked in the input
 
-../scripts/run_inference.py inference.output_prefix=example_outputs/design_motifscaffolding_inpaintseq inference.input_pdb=input_pdbs/5TPN.pdb 'contigmap.contigs=[10-40/A163-181/10-40]' inference.num_designs=10 'contigmap.inpaint_seq=[A163-168/A170-171/A179]'
+../scripts/run_inference.py inference.output_prefix=example_outputs/design_motifscaffolding_inpaintseq inference.input_pdb=input_pdbs/5TPN.pdb 'contigmap.contigs=[10-40/A163-181/10-40]' inference.num_designs=2 'contigmap.inpaint_seq=[A163-168/A170-171/A179]'
diff --git a/examples/design_motifscaffolding_with_target.sh b/examples/design_motifscaffolding_with_target.sh
@@ -11,4 +11,4 @@
 # We generate 10 designs
 # As in the paper (at least for some of the designs we tested), we use the complex-finetuned model
 
-python ../scripts/run_inference.py inference.output_prefix=example_outputs/design_motifscaffolding_with_target inference.input_pdb=input_pdbs/1YCR.pdb 'contigmap.contigs=[A25-109/0 0-70/B17-29/0-70]' contigmap.length=70-120 inference.num_designs=10 inference.ckpt_override_path=../models/Complex_base_ckpt.pt
+python ../scripts/run_inference.py inference.output_prefix=example_outputs/design_motifscaffolding_with_target inference.input_pdb=input_pdbs/1YCR.pdb 'contigmap.contigs=[A25-109/0 0-70/B17-29/0-70]' contigmap.length=70-120 inference.num_designs=2 inference.ckpt_override_path=../models/Complex_base_ckpt.pt
diff --git a/examples/design_nickel.sh b/examples/design_nickel.sh
@@ -17,4 +17,4 @@
 
 ckpt='../models/Base_epoch8_ckpt.pt'
 
-python ../scripts/run_inference.py inference.symmetry="C4" inference.num_designs=15 inference.output_prefix=example_outputs/design_nickel 'potentials.guiding_potentials=["type:olig_contacts,weight_intra:1,weight_inter:0.06"]' potentials.olig_intra_all=True potentials.olig_inter_all=True potentials.guide_scale=2 potentials.guide_decay="quadratic" inference.input_pdb=input_pdbs/nickel_symmetric_motif.pdb 'contigmap.contigs=[50/A2-4/50/0 50/A7-9/50/0 50/A12-14/50/0 50/A17-19/50/0]' inference.ckpt_override_path=$ckpt
+python ../scripts/run_inference.py inference.symmetry="C4" inference.num_designs=2 inference.output_prefix=example_outputs/design_nickel 'potentials.guiding_potentials=["type:olig_contacts,weight_intra:1,weight_inter:0.06"]' potentials.olig_intra_all=True potentials.olig_inter_all=True potentials.guide_scale=2 potentials.guide_decay="quadratic" inference.input_pdb=input_pdbs/nickel_symmetric_motif.pdb 'contigmap.contigs=[50/A2-4/50/0 50/A7-9/50/0 50/A12-14/50/0 50/A17-19/50/0]' inference.ckpt_override_path=$ckpt
diff --git a/examples/design_partialdiffusion.sh b/examples/design_partialdiffusion.sh
@@ -7,4 +7,4 @@
 # But, in either case, the contig length must sum to the length of the input pdb file
 # We generate 10 designs, and noise and denoise 10 steps (20% of the full trajectory)
 
-../scripts/run_inference.py inference.output_prefix=example_outputs/design_partialdiffusion inference.input_pdb=input_pdbs/2KL8.pdb 'contigmap.contigs=[79-79]' inference.num_designs=10 diffuser.partial_T=10
+../scripts/run_inference.py inference.output_prefix=example_outputs/design_partialdiffusion inference.input_pdb=input_pdbs/2KL8.pdb 'contigmap.contigs=[79-79]' inference.num_designs=2 diffuser.partial_T=10
diff --git a/examples/design_partialdiffusion_multipleseq.sh b/examples/design_partialdiffusion_multipleseq.sh
@@ -6,4 +6,4 @@
 # Note the ranges do not necessarily need to lie on the same chain as in this example.
 # However, positions are 0-indexed over the whole sequence--not per-chain-- so care must be taken when providing ranges to provide_seq.
 
-../scripts/run_inference.py inference.output_prefix=example_outputs/design_partialdiffusion_peptidewithmultiplesequence inference.input_pdb=input_pdbs/peptide_complex_ideal_helix.pdb 'contigmap.contigs=["172-172/0 34-34"]' diffuser.partial_T=10 inference.num_designs=10 'contigmap.provide_seq=[172-177,200-205]'
+../scripts/run_inference.py inference.output_prefix=example_outputs/design_partialdiffusion_peptidewithmultiplesequence inference.input_pdb=input_pdbs/peptide_complex_ideal_helix.pdb 'contigmap.contigs=["172-172/0 34-34"]' diffuser.partial_T=10 inference.num_designs=2 'contigmap.provide_seq=[172-177,200-205]'
diff --git a/examples/design_partialdiffusion_withseq.sh b/examples/design_partialdiffusion_withseq.sh
@@ -11,4 +11,4 @@
 # This contig will lead to the whole input pdb being noise by 10 steps (partial_T=10)
 # However, we provide the sequence of the peptide (the last 20 residues in the contig), with provide_seq=[172-205]. This is 0-indexed
 
-../scripts/run_inference.py inference.output_prefix=example_outputs/design_partialdiffusion_peptidewithsequence inference.input_pdb=input_pdbs/peptide_complex_ideal_helix.pdb 'contigmap.contigs=["172-172/0 34-34"]' diffuser.partial_T=10 inference.num_designs=10 'contigmap.provide_seq=[172-205]'
+../scripts/run_inference.py inference.output_prefix=example_outputs/design_partialdiffusion_peptidewithsequence inference.input_pdb=input_pdbs/peptide_complex_ideal_helix.pdb 'contigmap.contigs=["172-172/0 34-34"]' diffuser.partial_T=10 inference.num_designs=2 'contigmap.provide_seq=[172-205]'
diff --git a/examples/design_ppi.sh b/examples/design_ppi.sh
@@ -8,4 +8,4 @@
 # We tell diffusion to target three specific residues on the target, specifically residues 59, 83 and 91 of the A chain
 # We make 10 designs, and reduce the noise added during inference to 0, to improve the quality of the designs
 
-../scripts/run_inference.py inference.output_prefix=example_outputs/design_ppi inference.input_pdb=input_pdbs/insulin_target.pdb 'contigmap.contigs=[A1-150/0 70-100]' 'ppi.hotspot_res=[A59,A83,A91]' inference.num_designs=10 denoiser.noise_scale_ca=0 denoiser.noise_scale_frame=0
+../scripts/run_inference.py inference.output_prefix=example_outputs/design_ppi inference.input_pdb=input_pdbs/insulin_target.pdb 'contigmap.contigs=[A1-150/0 70-100]' 'ppi.hotspot_res=[A59,A83,A91]' inference.num_designs=2 denoiser.noise_scale_ca=0 denoiser.noise_scale_frame=0
diff --git a/examples/design_ppi_flexible_peptide.sh b/examples/design_ppi_flexible_peptide.sh
@@ -9,4 +9,4 @@
 # We make 10 designs
 # We mask (diffuse) the structure of the peptide using the inpaint_str flag. This has the effect of having RFdiffusion simultaneously design a binder and predict the structure of the peptide within the complex.
 
-../scripts/run_inference.py inference.output_prefix=example_outputs/design_ppi_flexible_peptide inference.input_pdb=input_pdbs/3IOL.pdb 'contigmap.contigs=[B10-35/0 70-100]' 'ppi.hotspot_res=[B28,B29]' inference.num_designs=10 'contigmap.inpaint_str=[B10-35]'
+../scripts/run_inference.py inference.output_prefix=example_outputs/design_ppi_flexible_peptide inference.input_pdb=input_pdbs/3IOL.pdb 'contigmap.contigs=[B10-35/0 70-100]' 'ppi.hotspot_res=[B28,B29]' inference.num_designs=2 'contigmap.inpaint_str=[B10-35]'
diff --git a/examples/design_ppi_flexible_peptide_with_secondarystructure_specification.sh b/examples/design_ppi_flexible_peptide_with_secondarystructure_specification.sh
@@ -10,4 +10,4 @@
 # We then specify that we want this to adopt a helical conformation
 # If you wanted to specify that it should adopt a strand conformation, you would specify `contigmap.inpaint_str_strand`
 
-../scripts/run_inference.py inference.output_prefix=example_outputs/design_ppi_flexible_peptide_with_secondarystructure inference.input_pdb=input_pdbs/tau_peptide.pdb 'contigmap.contigs=[70-100/0 B165-178]' inference.num_designs=10 'contigmap.inpaint_str=[B165-178]' scaffoldguided.scaffoldguided=True 'contigmap.inpaint_str_helix=[B165-178]'
+../scripts/run_inference.py inference.output_prefix=example_outputs/design_ppi_flexible_peptide_with_secondarystructure inference.input_pdb=input_pdbs/tau_peptide.pdb 'contigmap.contigs=[70-100/0 B165-178]' inference.num_designs=2 'contigmap.inpaint_str=[B165-178]' scaffoldguided.scaffoldguided=True 'contigmap.inpaint_str_helix=[B165-178]'
diff --git a/examples/design_ppi_scaffolded.sh b/examples/design_ppi_scaffolded.sh
@@ -7,4 +7,4 @@
 # We then provide a path to a directory of different scaffolds (we've provided some for you to use, from Cao et al., 2022)
 # We generate 10 designs, and reduce the noise added during inference to 0 (which improves the quality of designs)
 
-../scripts/run_inference.py scaffoldguided.target_path=input_pdbs/insulin_target.pdb inference.output_prefix=example_outputs/design_ppi_scaffolded scaffoldguided.scaffoldguided=True 'ppi.hotspot_res=[A59,A83,A91]' scaffoldguided.target_pdb=True scaffoldguided.target_ss=target_folds/insulin_target_ss.pt scaffoldguided.target_adj=target_folds/insulin_target_adj.pt scaffoldguided.scaffold_dir=./ppi_scaffolds/ inference.num_designs=10 denoiser.noise_scale_ca=0 denoiser.noise_scale_frame=0
+../scripts/run_inference.py scaffoldguided.target_path=input_pdbs/insulin_target.pdb inference.output_prefix=example_outputs/design_ppi_scaffolded scaffoldguided.scaffoldguided=True 'ppi.hotspot_res=[A59,A83,A91]' scaffoldguided.target_pdb=True scaffoldguided.target_ss=target_folds/insulin_target_ss.pt scaffoldguided.target_adj=target_folds/insulin_target_adj.pt scaffoldguided.scaffold_dir=./ppi_scaffolds/ inference.num_designs=2 denoiser.noise_scale_ca=0 denoiser.noise_scale_frame=0
diff --git a/examples/design_tetrahedral_oligos.sh b/examples/design_tetrahedral_oligos.sh
@@ -7,4 +7,4 @@
 # We decay this potential with quadratic form, so that it is applied more strongly initially
 # We specify a total length of 1200aa, so each chain is 100 residues long
 
-python ../scripts/run_inference.py --config-name=symmetry inference.symmetry="tetrahedral" inference.num_designs=10 inference.output_prefix="example_outputs/tetrahedral_oligo" 'potentials.guiding_potentials=["type:olig_contacts,weight_intra:1,weight_inter:0.1"]' potentials.olig_intra_all=True potentials.olig_inter_all=True potentials.guide_scale=2.0 potentials.guide_decay="quadratic" 'contigmap.contigs=[1200-1200]'
+python ../scripts/run_inference.py --config-name=symmetry inference.symmetry="tetrahedral" inference.num_designs=2 inference.output_prefix="example_outputs/tetrahedral_oligo" 'potentials.guiding_potentials=["type:olig_contacts,weight_intra:1,weight_inter:0.1"]' potentials.olig_intra_all=True potentials.olig_inter_all=True potentials.guide_scale=2.0 potentials.guide_decay="quadratic" 'contigmap.contigs=[1200-1200]'
diff --git a/examples/design_timbarrel.sh b/examples/design_timbarrel.sh
@@ -8,4 +8,4 @@
 # We add 0-5 residues (randomly sampled) to the N and the C-terminus
 # This will allow the generation of diverse TIM barrels with slightly different length helices and strands
 
-../scripts/run_inference.py inference.output_prefix=example_outputs/design_tim_barrel scaffoldguided.scaffoldguided=True scaffoldguided.target_pdb=False scaffoldguided.scaffold_dir=tim_barrel_scaffold/ inference.num_designs=10 denoiser.noise_scale_ca=0.5 denoiser.noise_scale_frame=0.5 scaffoldguided.sampled_insertion=0-5 scaffoldguided.sampled_N=0-5 scaffoldguided.sampled_C=0-5
+../scripts/run_inference.py inference.output_prefix=example_outputs/design_tim_barrel scaffoldguided.scaffoldguided=True scaffoldguided.target_pdb=False scaffoldguided.scaffold_dir=tim_barrel_scaffold/ inference.num_designs=2 denoiser.noise_scale_ca=0.5 denoiser.noise_scale_frame=0.5 scaffoldguided.sampled_insertion=0-5 scaffoldguided.sampled_N=0-5 scaffoldguided.sampled_C=0-5
diff --git a/examples/design_unconditional.sh b/examples/design_unconditional.sh
@@ -4,4 +4,4 @@
 # We tell RFdiffusion that designs should be 100-200 residues in length (randomly sampled each design)
 # We generate 10 such designs
 
-../scripts/run_inference.py inference.output_prefix=example_outputs/design_unconditional 'contigmap.contigs=[100-200]' inference.num_designs=10
+../scripts/run_inference.py inference.output_prefix=example_outputs/design_unconditional 'contigmap.contigs=[100-200]' inference.num_designs=2
diff --git a/examples/design_unconditional_w_contact_potential.sh b/examples/design_unconditional_w_contact_potential.sh
@@ -4,4 +4,4 @@
 # We tell RFdiffusion that designs should be 100-200 residues in length (randomly sampled each design)
 # We generate 10 such designs
 
-../scripts/run_inference.py inference.output_prefix=example_outputs/design_unconditional_w_contact_potential 'contigmap.contigs=[100-200]' inference.num_designs=10 'potentials.guiding_potentials=["type:monomer_contacts,weight:0.05"]'
+../scripts/run_inference.py inference.output_prefix=example_outputs/design_unconditional_w_contact_potential 'contigmap.contigs=[100-200]' inference.num_designs=2 'potentials.guiding_potentials=["type:monomer_contacts,weight:0.05"]'
diff --git a/examples/design_unconditional_w_monomer_ROG.sh b/examples/design_unconditional_w_monomer_ROG.sh
@@ -7,4 +7,4 @@
 # We use the monomer_ROG potential, with guide scale 2 and quadratic decay
 # Note that this potential is probably not necessary in this kind of case, but is provided as an example
 
-../scripts/run_inference.py inference.output_prefix=example_outputs/design_monomer_ROG_unconditional 'contigmap.contigs=[100-200]' inference.num_designs=10 'potentials.guiding_potentials=["type:monomer_ROG,weight:1,min_dist:5"]' potentials.guide_scale=2 potentials.guide_decay="quadratic"
+../scripts/run_inference.py inference.output_prefix=example_outputs/design_monomer_ROG_unconditional 'contigmap.contigs=[100-200]' inference.num_designs=2 'potentials.guiding_potentials=["type:monomer_ROG,weight:1,min_dist:5"]' potentials.guide_scale=2 potentials.guide_decay="quadratic"

Original file line number	Diff line number	Diff line change
`@@ -17,4 +17,4 @@`
`17`	`17`
`18`	`18`	`ckpt='../models/Base_epoch8_ckpt.pt'`
`19`	`19`
`20`		-python ../scripts/run_inference.py inference.symmetry="C4" inference.num_designs=15 inference.output_prefix=example_outputs/design_nickel 'potentials.guiding_potentials=["type:olig_contacts,weight_intra:1,weight_inter:0.06"]' potentials.olig_intra_all=True potentials.olig_inter_all=True potentials.guide_scale=2 potentials.guide_decay="quadratic" inference.input_pdb=input_pdbs/nickel_symmetric_motif.pdb 'contigmap.contigs=[50/A2-4/50/0 50/A7-9/50/0 50/A12-14/50/0 50/A17-19/50/0]' inference.ckpt_override_path=$ckpt
	`20`	+python ../scripts/run_inference.py inference.symmetry="C4" inference.num_designs=2 inference.output_prefix=example_outputs/design_nickel 'potentials.guiding_potentials=["type:olig_contacts,weight_intra:1,weight_inter:0.06"]' potentials.olig_intra_all=True potentials.olig_inter_all=True potentials.guide_scale=2 potentials.guide_decay="quadratic" inference.input_pdb=input_pdbs/nickel_symmetric_motif.pdb 'contigmap.contigs=[50/A2-4/50/0 50/A7-9/50/0 50/A12-14/50/0 50/A17-19/50/0]' inference.ckpt_override_path=$ckpt