Merge pull request #1163 from bghira/feature/validation-lycoris-strength

add more info to model card, refine contents

helpers/publishing/metadata.py

@@ -299,7 +299,9 @@ def lora_info(args):
 def model_card_note(args):
     """Return a string with the model card note."""
     note_contents = args.model_card_note if args.model_card_note else ""
-    return f"\n{note_contents}\n"
+    if note_contents is None or note_contents == "":
+        return ""
+    return f"\n**Note:** {note_contents}\n"
 
 
 def flux_schedule_info(args):
@@ -312,6 +314,7 @@ def flux_schedule_info(args):
         output_args.append("flux_schedule_auto_shift")
     if args.flux_schedule_shift is not None:
         output_args.append(f"shift={args.flux_schedule_shift}")
+    output_args.append(f"flux_guidance_mode={args.flux_guidance_mode}")
     if args.flux_guidance_value:
         output_args.append(f"flux_guidance_value={args.flux_guidance_value}")
     if args.flux_guidance_min:
@@ -324,6 +327,9 @@ def flux_schedule_info(args):
         output_args.append(f"flux_beta_schedule_beta={args.flux_beta_schedule_beta}")
     if args.flux_attention_masked_training:
         output_args.append("flux_attention_masked_training")
+    if args.t5_padding != "unmodified":
+        output_args.append(f"t5_padding={args.t5_padding}")
+    output_args.append(f"flow_matching_loss={args.flow_matching_loss}")
     if (
         args.model_type == "lora"
         and args.lora_type == "standard"
@@ -362,6 +368,7 @@ def sd3_schedule_info(args):
 
     return output_str
 
+
 def ddpm_schedule_info(args):
     """Information about DDPM schedules, eg. rescaled betas or offset noise"""
     output_args = []
@@ -370,15 +377,21 @@ def ddpm_schedule_info(args):
     if args.use_soft_min_snr:
         output_args.append(f"use_soft_min_snr")
         if args.soft_min_snr_sigma_data:
-            output_args.append(f"soft_min_snr_sigma_data={args.soft_min_snr_sigma_data}")
+            output_args.append(
+                f"soft_min_snr_sigma_data={args.soft_min_snr_sigma_data}"
+            )
     if args.rescale_betas_zero_snr:
         output_args.append(f"rescale_betas_zero_snr")
     if args.offset_noise:
         output_args.append(f"offset_noise")
         output_args.append(f"noise_offset={args.noise_offset}")
         output_args.append(f"noise_offset_probability={args.noise_offset_probability}")
-    output_args.append(f"training_scheduler_timestep_spacing={args.training_scheduler_timestep_spacing}")
-    output_args.append(f"validation_scheduler_timestep_spacing={args.validation_scheduler_timestep_spacing}")
+    output_args.append(
+        f"training_scheduler_timestep_spacing={args.training_scheduler_timestep_spacing}"
+    )
+    output_args.append(
+        f"validation_scheduler_timestep_spacing={args.validation_scheduler_timestep_spacing}"
+    )
     output_str = (
         f" (extra parameters={output_args})"
         if output_args
@@ -387,6 +400,7 @@ def ddpm_schedule_info(args):
 
     return output_str
 
+
 def model_schedule_info(args):
     if args.model_family == "flux":
         return flux_schedule_info(args)
@@ -396,7 +410,6 @@ def model_schedule_info(args):
         return ddpm_schedule_info(args)
 
 
-
 def save_model_card(
     repo_id: str,
     images=None,
@@ -488,18 +501,19 @@ def save_model_card(
 
 {'This is a **diffusion** model trained using DDPM objective instead of Flow matching. **Be sure to set the appropriate scheduler configuration.**' if args.model_family == "sd3" and args.flow_matching_loss == "diffusion" else ''}
 {'The main validation prompt used during training was:' if prompt else 'Validation used ground-truth images as an input for partial denoising (img2img).' if args.validation_using_datasets else 'No validation prompt was used during training.'}
-{model_card_note(args)}
 {'```' if prompt else ''}
 {prompt}
 {'```' if prompt else ''}
 
+{model_card_note(args)}
 ## Validation settings
 - CFG: `{StateTracker.get_args().validation_guidance}`
 - CFG Rescale: `{StateTracker.get_args().validation_guidance_rescale}`
 - Steps: `{StateTracker.get_args().validation_num_inference_steps}`
-- Sampler: `{StateTracker.get_args().validation_noise_scheduler}`
+- Sampler: `{'FlowMatchEulerDiscreteScheduler' if args.model_family in ['sd3', 'flux'] else StateTracker.get_args().validation_noise_scheduler}`
 - Seed: `{StateTracker.get_args().validation_seed}`
 - Resolution{'s' if ',' in StateTracker.get_args().validation_resolution else ''}: `{StateTracker.get_args().validation_resolution}`
+{f"- Skip-layer guidance: {_skip_layers(args)}" if args.model_family in ['sd3', 'flux'] else ''}
 
 Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
 

tests/test_model_card.py

@@ -36,7 +36,7 @@ def setUp(self):
         self.args.lora_init_type = "kaiming_uniform"
         self.args.model_card_note = "Test note"
         self.args.validation_using_datasets = False
-        self.args.flow_matching_loss = "flow-matching"
+        self.args.flow_matching_loss = "compatible"
         self.args.flux_fast_schedule = False
         self.args.flux_schedule_auto_shift = False
         self.args.flux_schedule_shift = None
@@ -61,6 +61,9 @@ def setUp(self):
         self.args.optimizer_config = ""
         self.args.mixed_precision = "fp16"
         self.args.base_model_precision = "no_change"
+        self.args.flux_guidance_mode = "constant"
+        self.args.flux_guidance_value = 1.0
+        self.args.t5_padding = "unmodified"
         self.args.enable_xformers_memory_efficient_attention = False
 
     def test_model_imports(self):
@@ -203,7 +206,10 @@ def test_model_card_note(self):
     def test_flux_schedule_info(self):
         self.args.model_family = "flux"
         output = flux_schedule_info(self.args)
-        self.assertIn("(no special parameters set)", output)
+        self.assertEqual(
+            " (extra parameters=['flux_guidance_mode=constant', 'flux_guidance_value=1.0', 'flow_matching_loss=compatible'])",
+            output,
+        )
 
         self.args.flux_fast_schedule = True
         output = flux_schedule_info(self.args)