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Fix autocast device type

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cellzero
2025-01-20 20:21:28 +08:00
parent f6543017c9
commit 6c7b5972dc
3 changed files with 7 additions and 3 deletions
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comfyui/comfyui_nodes.py
+5 -1
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@@ -549,7 +549,7 @@ class Ruyi_I2VSampler:
self.initialize_plugins(pipeline, plugin_parameters, steps)
# Inference
with torch.no_grad(), torch.autocast(str(device), dtype = pipeline.transformer.dtype):
with torch.no_grad(), torch.autocast(device_type=device.type, dtype=pipeline.transformer.dtype):
video_length = int(video_length // pipeline.vae.mini_batch_encoder * pipeline.vae.mini_batch_encoder) if video_length != 1 else 1
input_video, input_video_mask, clip_image = get_image_to_video_latent(start_img, end_img, video_length=video_length, sample_size=(height, width))
@@ -583,6 +583,10 @@ class Ruyi_I2VSampler:
for _lora_path, _lora_weight in zip(ruyi_model.get("loras", []), ruyi_model.get("strength_model", [])):
pipeline = unmerge_lora(pipeline, _lora_path, _lora_weight)
# Log information
if pipeline.transformer.tea_cache.enabled:
print("TeaCache cached steps:", pipeline.transformer.tea_cache.skip_count)
return (videos,)
predict_i2v.py
+1 -1
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@@ -270,7 +270,7 @@ pipeline.transformer.tea_cache.initialize(tea_cache_enabled, tea_cache_threshold
pipeline.transformer.enhance_a_video.initialize(enhance_a_video_enabled, enhance_a_video_weight, enhance_a_video_skip_start_steps, enhance_a_video_skip_end_steps, steps)
# Generate video
with torch.no_grad(), torch.autocast(str(device), dtype = pipeline.transformer.dtype):
with torch.no_grad(), torch.autocast(device_type=device.type, dtype=pipeline.transformer.dtype):
video_length = int(video_length // pipeline.vae.mini_batch_encoder * pipeline.vae.mini_batch_encoder) if video_length != 1 else 1
input_video, input_video_mask, clip_image = get_image_to_video_latent(start_img, end_img, video_length=video_length, sample_size=(height, width))
predict_i2v_80g.py
+1 -1
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@@ -270,7 +270,7 @@ pipeline.transformer.tea_cache.initialize(tea_cache_enabled, tea_cache_threshold
pipeline.transformer.enhance_a_video.initialize(enhance_a_video_enabled, enhance_a_video_weight, enhance_a_video_skip_start_steps, enhance_a_video_skip_end_steps, steps)
# Generate video
with torch.no_grad(), torch.autocast(str(device), dtype = pipeline.transformer.dtype):
with torch.no_grad(), torch.autocast(device_type=device.type, dtype=pipeline.transformer.dtype):
video_length = int(video_length // pipeline.vae.mini_batch_encoder * pipeline.vae.mini_batch_encoder) if video_length != 1 else 1
input_video, input_video_mask, clip_image = get_image_to_video_latent(start_img, end_img, video_length=video_length, sample_size=(height, width))