nodes/MrForExample-ComfyUI-3D-Pack
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Merge pull request #513 from DenisKochetov/trellis-fix-torch

Browse Source 
fix trellis for updated torch
This commit is contained in:
Mr. For Example
2025-07-24 18:23:24 +04:00
committed by GitHub
parent a1dab7a9a0 f6f262df31
commit 6fa0039ad5
1 changed files with 54 additions and 52 deletions
Download Patch File Download Diff File Expand all files Collapse all files
Gen_3D_Modules/TRELLIS/trellis/utils/postprocessing_utils.py
+54 -52
View File
@@ -345,59 +345,61 @@ def bake_texture(
texture = cv2.inpaint(texture, mask, 3, cv2.INPAINT_TELEA)
elif mode == 'opt':
rastctx = utils3d.torch.RastContext(backend='cuda')
observations = [observations.flip(0) for observations in observations]
masks = [m.flip(0) for m in masks]
_uv = []
_uv_dr = []
for i, (observation, view, projection) in enumerate(tqdm(zip(observations, views, projections), total=steps, disable=not verbose, desc='Texture baking (opt): UV')):
with torch.no_grad():
rast = utils3d.torch.rasterize_triangle_faces(
rastctx, vertices[None], faces, observation.shape[1], observation.shape[0], uv=uvs[None], view=view, projection=projection
)
_uv.append(rast['uv'].detach())
_uv_dr.append(rast['uv_dr'].detach())
with torch.inference_mode(False):
rastctx = utils3d.torch.RastContext(backend='cuda')
observations = [observations.flip(0) for observations in observations]
masks = [m.flip(0) for m in masks]
_uv = []
_uv_dr = []
for observation, view, projection in tqdm(zip(observations, views, projections), total=len(views),
disable=not verbose, desc='Texture baking (opt): UV'):
with torch.no_grad():
rast = utils3d.torch.rasterize_triangle_faces(
rastctx, vertices[None], faces, observation.shape[1], observation.shape[0], uv=uvs[None],
view=view, projection=projection
)
_uv.append(rast['uv'].detach())
_uv_dr.append(rast['uv_dr'].detach())
texture = torch.nn.Parameter(torch.zeros((1, texture_size, texture_size, 3), dtype=torch.float32).cuda())
optimizer = torch.optim.Adam([texture], betas=(0.5, 0.9), lr=1e-2)
comfy_pbar.update_absolute(i + 1)
texture = torch.nn.Parameter(torch.zeros((1, texture_size, texture_size, 3), dtype=torch.float32).cuda())
optimizer = torch.optim.Adam([texture], betas=(0.5, 0.9), lr=1e-2)
def exp_anealing(optimizer, step, total_steps, start_lr, end_lr):
return start_lr * (end_lr / start_lr) ** (step / total_steps)
def cosine_anealing(optimizer, step, total_steps, start_lr, end_lr):
return end_lr + 0.5 * (start_lr - end_lr) * (1 + np.cos(np.pi * step / total_steps))
def tv_loss(texture):
return torch.nn.functional.l1_loss(texture[:, :-1, :, :], texture[:, 1:, :, :]) + \
torch.nn.functional.l1_loss(texture[:, :, :-1, :], texture[:, :, 1:, :])
total_steps = 2500
comfy_pbar = comfy.utils.ProgressBar(total_steps)
with tqdm(total=total_steps, disable=not verbose, desc='Texture baking (opt): optimizing') as pbar:
for step in range(total_steps):
optimizer.zero_grad()
selected = np.random.randint(0, len(views))
uv, uv_dr, observation, mask = _uv[selected], _uv_dr[selected], observations[selected], masks[selected]
render = dr.texture(texture, uv, uv_dr)[0]
loss = torch.nn.functional.l1_loss(render[mask], observation[mask])
if lambda_tv > 0:
loss += lambda_tv * tv_loss(texture)
loss.backward()
optimizer.step()
# annealing
optimizer.param_groups[0]['lr'] = cosine_anealing(optimizer, step, total_steps, 1e-2, 1e-5)
pbar.set_postfix({'loss': loss.item()})
pbar.update()
comfy_pbar.update_absolute(step + 1)
texture = np.clip(texture[0].flip(0).detach().cpu().numpy() * 255, 0, 255).astype(np.uint8)
mask = 1 - utils3d.torch.rasterize_triangle_faces(
rastctx, (uvs * 2 - 1)[None], faces, texture_size, texture_size
)['mask'][0].detach().cpu().numpy().astype(np.uint8)
texture = cv2.inpaint(texture, mask, 3, cv2.INPAINT_TELEA)
def exp_anealing(optimizer, step, total_steps, start_lr, end_lr):
return start_lr * (end_lr / start_lr) ** (step / total_steps)
def cosine_anealing(optimizer, step, total_steps, start_lr, end_lr):
return end_lr + 0.5 * (start_lr - end_lr) * (1 + np.cos(np.pi * step / total_steps))
def tv_loss(texture):
return torch.nn.functional.l1_loss(texture[:, :-1, :, :], texture[:, 1:, :, :]) + \
torch.nn.functional.l1_loss(texture[:, :, :-1, :], texture[:, :, 1:, :])
total_steps = 2500
with tqdm(total=total_steps, disable=not verbose, desc='Texture baking (opt): optimizing') as pbar:
for step in range(total_steps):
optimizer.zero_grad()
selected = np.random.randint(0, len(views))
uv, uv_dr, observation, mask = _uv[selected], _uv_dr[selected], observations[selected], masks[
selected]
render = dr.texture(texture, uv, uv_dr)[0]
loss = torch.nn.functional.l1_loss(render[mask], observation[mask])
if lambda_tv > 0:
loss += lambda_tv * tv_loss(texture)
# if loss.shape == torch.Size([]):
# loss.requires_grad = True # try fix element 0 of tensors does not require grad and does not have a grad_fn
loss.backward()
optimizer.step()
# annealing
optimizer.param_groups[0]['lr'] = cosine_anealing(optimizer, step, total_steps, 1e-2, 1e-5)
pbar.set_postfix({'loss': loss.item()})
pbar.update()
texture = np.clip(texture[0].flip(0).detach().cpu().numpy() * 255, 0, 255).astype(np.uint8)
mask = 1 - utils3d.torch.rasterize_triangle_faces(
rastctx, (uvs * 2 - 1)[None], faces, texture_size, texture_size
)['mask'][0].detach().cpu().numpy().astype(np.uint8)
texture = cv2.inpaint(texture, mask, 3, cv2.INPAINT_TELEA)
else:
raise ValueError(f'Unknown mode: {mode}')