Add sampler preview

latent_preview.py

@@ -0,0 +1,80 @@
+import io
+
+import torch
+from PIL import Image
+import struct
+import numpy as np
+from comfy.cli_args import args, LatentPreviewMethod
+from comfy.taesd.taesd import TAESD
+import comfy.model_management
+import folder_paths
+import comfy.utils
+import logging
+
+MAX_PREVIEW_RESOLUTION = args.preview_size
+
+def preview_to_image(latent_image):
+        latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1)  # change scale from -1..1 to 0..1
+                            .mul(0xFF)  # to 0..255
+                            ).to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device))
+
+        return Image.fromarray(latents_ubyte.numpy())
+
+class LatentPreviewer:
+    def decode_latent_to_preview(self, x0):
+        pass
+
+    def decode_latent_to_preview_image(self, preview_format, x0):
+        preview_image = self.decode_latent_to_preview(x0)
+        return ("GIF", preview_image, MAX_PREVIEW_RESOLUTION)
+
+class Latent2RGBPreviewer(LatentPreviewer):
+    def __init__(self):
+        #latent_rgb_factors =  [[0.05389399697934166, 0.025018778505575393, -0.009193515248318657], [0.02318250640590553, -0.026987363837713156, 0.040172639061236956], [0.046035451343323666, -0.02039565868920197, 0.01275569344290342], [-0.015559161155025095, 0.051403973219861246, 0.03179031307996347], [-0.02766167769640129, 0.03749545161530447, 0.003335141009473408], [0.05824598730479011, 0.021744367381243884, -0.01578925627951616], [0.05260929401500947, 0.0560165014956886, -0.027477296572565126], [0.018513891242931686, 0.041961785217662514, 0.004490763489747966], [0.024063060899760215, 0.065082853069653, 0.044343437673514896], [0.05250992323006226, 0.04361117432588933, 0.01030076055524387], [0.0038921710021782366, -0.025299228133723792, 0.019370764014574535], [-0.00011950534333568519, 0.06549370069727675, -0.03436712163379723], [-0.026020578032683626, -0.013341758571090847, -0.009119046570271953], [0.024412451175602937, 0.030135064560817174, -0.008355486384198006], [0.04002209845752687, -0.017341304390739463, 0.02818338690302971], [-0.032575108695213684, -0.009588338926775117, -0.03077312160940468]]
+        latent_rgb_factors = [[0.1236769792512748, 0.11775175335219157, -0.17700629766423637], [-0.08504104329270078, 0.026605813147523694, -0.006843165704926019], [-0.17093308616366876, 0.027991854696200386, 0.14179146288816308], [-0.17179555328757623, 0.09844317368603078, 0.14470997015982784], [-0.16975067171668484, -0.10739852629856643, -0.1894254942909962], [-0.19315259266769888, -0.011029760569485209, -0.08519702054654255], [-0.08399895091432583, -0.0964246452052032, -0.033622359523655665], [0.08148916330842498, 0.027500645903400067, -0.06593099749891196], [0.0456603103902293, -0.17844808072462398, 0.04204775167149785], [0.001751626383204502, -0.030567890189647867, -0.022078082809772193], [0.05110631095056278, -0.0709677393548804, 0.08963683539504264], [0.010515800868829, -0.18382052841762514, -0.08554553339721907]]
+
+        self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu").transpose(0, 1)
+        self.latent_rgb_factors_bias = None
+        # if latent_rgb_factors_bias is not None:
+        #     self.latent_rgb_factors_bias = torch.tensor(latent_rgb_factors_bias, device="cpu")
+
+    def decode_latent_to_preview(self, x0):
+        self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device)
+        if self.latent_rgb_factors_bias is not None:
+            self.latent_rgb_factors_bias = self.latent_rgb_factors_bias.to(dtype=x0.dtype, device=x0.device)
+
+        latent_image = torch.nn.functional.linear(x0[0].permute(1, 2, 0), self.latent_rgb_factors,
+                                                    bias=self.latent_rgb_factors_bias)
+        return preview_to_image(latent_image)
+
+
+def get_previewer():
+    previewer = None
+    method = args.preview_method
+    if method != LatentPreviewMethod.NoPreviews:
+        # TODO previewer method
+
+        if method == LatentPreviewMethod.Auto:
+            method = LatentPreviewMethod.Latent2RGB
+
+        if previewer is None:
+            previewer = Latent2RGBPreviewer()
+    return previewer
+
+def prepare_callback(model, steps, x0_output_dict=None):
+    preview_format = "JPEG"
+    if preview_format not in ["JPEG", "PNG"]:
+        preview_format = "JPEG"
+
+    previewer = get_previewer()
+
+    pbar = comfy.utils.ProgressBar(steps)
+    def callback(step, x0, x, total_steps):
+        if x0_output_dict is not None:
+            x0_output_dict["x0"] = x0
+        preview_bytes = None
+        if previewer:
+            preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0)
+        pbar.update_absolute(step + 1, total_steps, preview_bytes)
+    return callback
+

mochi_preview/t2v_synth_mochi.py

@@ -247,6 +247,9 @@ class T2VSynthMochiModel:
             ), f"sigma_schedule must have length {sample_steps + 1}, got {len(sigma_schedule)}"
         assert (num_frames - 1) % 6 == 0, f"t - 1 must be divisible by 6, got {num_frames - 1}"
 
+        from ..latent_preview import prepare_callback
+        callback = prepare_callback(self.dit, sample_steps)
+
         # create z
         spatial_downsample = 8
         temporal_downsample = 6
@@ -265,7 +268,7 @@ class T2VSynthMochiModel:
             dtype=torch.float32,
         )
         if in_samples is not None:
-            z = z * sigma_schedule[0] + in_samples.to(self.device) * sigma_schedule[-1]
+            z = z * sigma_schedule[0] + in_samples.to(self.device) * sigma_schedule[-2]
 
         sample = {
         "y_mask": [args["positive_embeds"]["attention_mask"].to(self.device)],
@@ -314,6 +317,9 @@ class T2VSynthMochiModel:
             )
             pred = pred.to(z)
             z = z + dsigma * pred
+            if callback is not None:
+                callback(i, z.detach()[0].permute(1,0,2,3), None, sample_steps)
+            else:
                 comfy_pbar.update(1)
        
         self.dit.to(self.offload_device)

nodes.py

@@ -469,6 +469,7 @@ class MochiSampler:
                 "attention_mask": negative[0][1]["attention_mask"].bool(),
                 }
             
+
         args = {
             "height": height,
             "width": width,