storytold/FineTrainers-Conditioning
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removed some code made things clearer to replace the pipeline inference which is needed for validation.

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CrossProduct
2025-01-23 23:49:24 +00:00
parent d6b3615421
commit b7f26bd2e6
4 changed files with 63 additions and 24 deletions
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finetrainers/trainer.py
+22 -18
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@@ -676,6 +676,7 @@ class Trainer:
if random.random() < self.args.caption_dropout_p:
prompts = [""] * batch_size
if self.pose_condition == True:
# this is vae output of target video
latent_conditions = condition_latents_prepare.prepare_latents_for_conditioning(
vae=self.vae,
image_or_video=videos,
@@ -726,7 +727,7 @@ class Trainer:
latent_conditions = make_contiguous(latent_conditions)
if self.pose_condition:
# VAE output not patchified yet.
# Pose Template VAE.
pose_video_latents = condition_latents_prepare.prepare_latents_for_conditioning(
vae=self.vae,
image_or_video=poses,
@@ -739,6 +740,7 @@ class Trainer:
pose_video_latents = make_contiguous(pose_video_latents)
# Image Ref VAE.
img_refs_latents = condition_latents_prepare.prepare_latents_for_conditioning(
vae=self.vae,
image_or_video=img_refs,
@@ -751,8 +753,6 @@ class Trainer:
img_refs_latents = make_contiguous(img_refs_latents)
text_conditions = make_contiguous(text_conditions)
if self.args.caption_dropout_technique == "zero":
@@ -776,13 +776,14 @@ class Trainer:
device=accelerator.device,
generator=self.state.generator,
)
timesteps = (sigmas * 1000.0).long()
noise = torch.randn(
latent_conditions["latents"].shape,
generator=self.state.generator,
device=accelerator.device,
dtype=weight_dtype,
latent_conditions["latents"].shape,
generator=self.state.generator,
device=accelerator.device,
dtype=weight_dtype,
)
sigmas = expand_tensor_dims(sigmas, ndim=noise.ndim)
@@ -797,38 +798,41 @@ class Trainer:
timesteps=timesteps,
)
else:
if self.pose_condition:
# Note in pose mode all shapes are VAE output shape and not patchified until the end
# condition with the pose information
# normalize and patchify
# create noise for latent to feed diffusion transformer to get pred noise
noisy_latents = (1.0 - sigmas) * latent_conditions["latents"] + sigmas * noise
noisy_latents = noisy_latents.to(latent_conditions["latents"].dtype)
target_video_latents = latent_conditions["latents"]
noisy_latents = (1.0 - sigmas) * target_video_latents + sigmas * noise
noisy_latents = noisy_latents.to(target_video_latents.dtype)
# add pose information to both channels
pose_noisy_latents = noisy_latents + pose_video_latents["latents"]
pose_img_ref_latents = img_refs_latents["latents"] + pose_video_latents["latents"]
# expand channel information # B x 2C latent will be projected to adapter to scale it back to 128d
latent_final = torch.cat([pose_img_ref_latents,pose_noisy_latents], dim=1)
# expand channel information # B x 2C latent will be projected to adapter to scale it back to 128d using adapter.
pose_img_ref_latents = torch.cat([pose_img_ref_latents,pose_noisy_latents], dim=1)
# project this turn into patches
latent_final = post_conditioned_latent_patchify(latents=latent_final,
pose_img_ref_latents = post_conditioned_latent_patchify(latents=pose_img_ref_latents,
num_frames=latent_conditions["num_frames"],
height=latent_conditions["height"],
width=latent_conditions["width"],
patch_size = 1,
patch_size_t = 1)
# target video as a input residual
noisy_latents_residual = post_conditioned_latent_patchify(latents=noisy_latents,
num_frames=latent_conditions["num_frames"],
height=latent_conditions["height"],
width=latent_conditions["width"],
patch_size = 1,
patch_size_t = 1)
# target latent patchified
# pose information latent
latent_conditions.update({"noisy_latents": latent_final["latents"]})
# Target Latent Patchified
# pose template noisey input [cat] img_ref + pose video
latent_conditions.update({"noisy_latents": pose_img_ref_latents["latents"]})
# input video noise at level residual information to adapter
latent_conditions.update({"noisy_latents_residual":noisy_latents_residual["latents"]})
else:
# Default to flow-matching noise addition