add builtin framepack

Signed-off-by: Vladimir Mandic <mandic00@live.com>
2026-09-20 09:38:23 +02:00 · 2025-07-08 15:47:07 -04:00
parent 239c3d6dd9
commit c559e26616
53 changed files with 3996 additions and 232 deletions
@@ -0,0 +1,96 @@
+import torch
+import einops
+from modules import shared, devices
+
+
+latent_rgb_factors = [ # from comfyui
+    [-0.0395, -0.0331, 0.0445],
+    [0.0696, 0.0795, 0.0518],
+    [0.0135, -0.0945, -0.0282],
+    [0.0108, -0.0250, -0.0765],
+    [-0.0209, 0.0032, 0.0224],
+    [-0.0804, -0.0254, -0.0639],
+    [-0.0991, 0.0271, -0.0669],
+    [-0.0646, -0.0422, -0.0400],
+    [-0.0696, -0.0595, -0.0894],
+    [-0.0799, -0.0208, -0.0375],
+    [0.1166, 0.1627, 0.0962],
+    [0.1165, 0.0432, 0.0407],
+    [-0.2315, -0.1920, -0.1355],
+    [-0.0270, 0.0401, -0.0821],
+    [-0.0616, -0.0997, -0.0727],
+    [0.0249, -0.0469, -0.1703]
+]
+latent_rgb_factors_bias = [0.0259, -0.0192, -0.0761]
+vae_weight = None
+vae_bias = None
+taesd = None
+
+
+def vae_decode_simple(latents):
+    global vae_weight, vae_bias # pylint: disable=global-statement
+    with devices.inference_context():
+        if vae_weight is None or vae_bias is None:
+            vae_weight = torch.tensor(latent_rgb_factors, device=devices.device, dtype=devices.dtype).transpose(0, 1)[:, :, None, None, None]
+            vae_bias = torch.tensor(latent_rgb_factors_bias, device=devices.device, dtype=devices.dtype)
+        images = torch.nn.functional.conv3d(latents, weight=vae_weight, bias=vae_bias, stride=1, padding=0, dilation=1, groups=1)
+        images = (images + 1.2) * 100 # sort-of normalized
+        images = einops.rearrange(images, 'b c t h w -> (b h) (t w) c')
+        images = images.to(torch.uint8).detach().cpu().numpy().clip(0, 255)
+    return images
+
+
+def vae_decode_tiny(latents):
+    global taesd # pylint: disable=global-statement
+    if taesd is None:
+        from modules import sd_vae_taesd
+        taesd = sd_vae_taesd.get_model(variant='TAE HunyuanVideo')
+        shared.log.debug(f'Video VAE: type=Tiny cls={taesd.__class__.__name__} latents={latents.shape}')
+    with devices.inference_context():
+        taesd = taesd.to(device=devices.device, dtype=devices.dtype)
+        latents = latents.transpose(1, 2) # pipe produces NCTHW and tae wants NTCHW
+        images = taesd.decode_video(latents, parallel=False, show_progress_bar=False)
+        images = images.transpose(1, 2).mul_(2).sub_(1) # normalize
+        taesd = taesd.to(device=devices.cpu, dtype=devices.dtype)
+    return images
+
+
+def vae_decode_remote(latents):
+    # from modules.sd_vae_remote import remote_decode
+    # images = remote_decode(latents, model_type='hunyuanvideo')
+    from diffusers.utils.remote_utils import remote_decode
+    images = remote_decode(
+        tensor=latents.contiguous(),
+        endpoint='https://o7ywnmrahorts457.us-east-1.aws.endpoints.huggingface.cloud',
+        output_type='pt',
+        return_type='pt',
+    )
+    return images
+
+
+def vae_decode_full(latents):
+    with devices.inference_context():
+        vae = shared.sd_model.vae
+        latents = (latents / vae.config.scaling_factor).to(device=vae.device, dtype=vae.dtype)
+        images = vae.decode(latents).sample
+    return images
+
+
+def vae_decode(latents, vae_type):
+    latents = latents.to(device=devices.device, dtype=devices.dtype)
+    if vae_type == 'Tiny':
+        return vae_decode_tiny(latents)
+    elif vae_type == 'Preview':
+        return vae_decode_simple(latents)
+    elif vae_type == 'Remote':
+        return vae_decode_remote(latents)
+    else: # vae_type == 'Full'
+        return vae_decode_full(latents)
+
+
+def vae_encode(image):
+    with devices.inference_context():
+        vae = shared.sd_model.vae
+        latents = vae.encode(image.to(device=vae.device, dtype=vae.dtype)).latent_dist.sample()
+        latents = latents * vae.config.scaling_factor
+    return latents