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mirror of https://github.com/vladmandic/automatic synced 2026-09-18 08:44:33 +02:00
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separate progress monitoring from live preview, live preview improvements, progress details

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2026-08-07 11:41:40 +02:00
parent b092cf7318
commit 48cf0166f3
12 changed files with 238 additions and 146 deletions
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modules/processing_callbacks.py
+5 -13
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@@ -61,6 +61,7 @@ def diffusers_callback(pipe, step: int = 0, timestep: int = 0, kwargs: dict | No
torch.xpu.synchronize(devices.device)
elif devices.backend in {"cuda", "zluda", "rocm"}:
torch.cuda.synchronize(devices.device)
time.sleep(0.001) # 1ms yield frees GIL for the preview thread
t1 = time.time()
@@ -82,7 +83,7 @@ def diffusers_callback(pipe, step: int = 0, timestep: int = 0, kwargs: dict | No
latents = kwargs.get('latents', None)
if debug:
debug_callback(f'Callback: step={step} timestep={timestep} latents={latents.shape if latents is not None else None} kwargs={list(kwargs)}')
debug_callback(f'Callback: step={step} timestep={timestep} latents={latents.shape if latents is not None else None} sync={shared.opts.torch_sync} kwargs={list(kwargs)}')
if shared.state.sampling_steps == 0 and getattr(pipe, 'num_timesteps', 0) > 0:
shared.state.sampling_steps = pipe.num_timesteps
shared.state.step()
@@ -91,14 +92,6 @@ def diffusers_callback(pipe, step: int = 0, timestep: int = 0, kwargs: dict | No
if latents is None or p is None:
return kwargs
"""
if torch.isnan(latents).any().item():
log.error(f'Callback: step={step} timestep={timestep} latents={latents.shape}:{latents.device}:{latents.dtype} error="contains NaN values"')
if (shared.state.current_latent is not None) and (shared.state.current_latent.shape == latents.shape):
log.error(f'Callback: step={step} timestep={timestep} latents={latents.shape}:{latents.device}:{latents.dtype} error="replacing with previous latent"')
latents = shared.state.current_latent
"""
if len(getattr(p, 'ip_adapter_names', [])) > 0 and p.ip_adapter_names[0] != 'None':
ip_adapter_scales = list(p.ip_adapter_scales)
ip_adapter_starts = list(p.ip_adapter_starts)
@@ -122,6 +115,7 @@ def diffusers_callback(pipe, step: int = 0, timestep: int = 0, kwargs: dict | No
cfg_end = getattr(p, "cfg_end", 1.0) or 1.0
total_steps = getattr(pipe, "num_timesteps", 0)
target_step = int(total_steps * cfg_end) if total_steps else 0
if (cfg_end < 1.0) and not getattr(pipe, "_cfg_end_applied", False) and (step >= target_step):
pipe._cfg_end_applied = True # pylint: disable=protected-access
if "PAG" in shared.sd_model.__class__.__name__:
@@ -145,7 +139,7 @@ def diffusers_callback(pipe, step: int = 0, timestep: int = 0, kwargs: dict | No
else:
width = getattr(p, 'width', 1024)
height = getattr(p, 'height', 1024)
shared.state.current_latent = pipe._unpack_latents(kwargs['latents'], height, width, pipe.vae_scale_factor) # pylint: disable=protected-access
shared.state.current_latent = pipe._unpack_latents(latents, height, width, pipe.vae_scale_factor) # pylint: disable=protected-access
if current_noise_pred is not None:
shared.state.current_noise_pred = pipe._unpack_latents(current_noise_pred, height, width, pipe.vae_scale_factor) # pylint: disable=protected-access
else:
@@ -158,7 +152,6 @@ def diffusers_callback(pipe, step: int = 0, timestep: int = 0, kwargs: dict | No
else:
width = getattr(p, 'width', 1024)
height = getattr(p, 'height', 1024)
latents = kwargs['latents']
if len(latents.shape) == 4:
latents = pipe._unpatchify_latents(latents) # [B, C*4, h/2, w/2] -> [B, C, h, w] # pylint: disable=protected-access
elif len(latents.shape) == 3: # packed format [B, seq_len, patch_channels]
@@ -183,7 +176,6 @@ def diffusers_callback(pipe, step: int = 0, timestep: int = 0, kwargs: dict | No
current_noise_pred = current_noise_pred.permute(0, 3, 1, 4, 2, 5).reshape(b, channels, h_patches * 2, w_patches * 2)
shared.state.current_noise_pred = current_noise_pred
elif 'Ideogram4' in pipe.__class__.__name__: # packed normalized [B, seq, 128] -> Flux.2 latent space for TAE FLUX.2
latents = kwargs['latents']
if latents.ndim == 3:
b, seq_len, packed_ch = latents.shape
vae_scale = getattr(pipe, 'vae_scale_factor', 8)
@@ -203,7 +195,7 @@ def diffusers_callback(pipe, step: int = 0, timestep: int = 0, kwargs: dict | No
shared.state.current_latent = latents
shared.state.current_noise_pred = current_noise_pred
else:
shared.state.current_latent = kwargs['latents']
shared.state.current_latent = latents
shared.state.current_noise_pred = current_noise_pred
# Video latent preview: extract middle frame from 5D [B,C,T,H,W] to 4D [B,C,H,W]