Update comments

examples/cogvideox_fun/predict_i2v.py

@@ -23,7 +23,7 @@ from videox_fun.pipeline import (CogVideoXFunInpaintPipeline,
 from videox_fun.utils import (apply_gpu_memory_mode, get_image_to_video_latent,
                               merge_lora, save_videos_grid, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -76,7 +76,7 @@ partial_video_length = None
 overlap_video_length = 4
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 # If you want to generate from text, please set the validation_image_start = None and validation_image_end = None
 validation_image_start  = "asset/1.png"
@@ -139,7 +139,7 @@ text_encoder = T5EncoderModel.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Euler": EulerDiscreteScheduler,
     "Euler A": EulerAncestralDiscreteScheduler,
     "DPM++": DPMSolverMultistepScheduler, 

examples/cogvideox_fun/predict_t2v.py

@@ -24,7 +24,7 @@ from videox_fun.pipeline import (CogVideoXFunInpaintPipeline,
 from videox_fun.utils import (apply_gpu_memory_mode, get_image_to_video_latent,
                               merge_lora, save_videos_grid, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -73,7 +73,7 @@ video_length        = 49
 fps                 = 8
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 prompt              = "A young woman with beautiful and clear eyes and blonde hair standing and white dress in a forest wearing a crown. She seems to be lost in thought, and the camera focuses on her face. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic."
 negative_prompt     = "The video is not of a high quality, it has a low resolution. Watermark present in each frame. The background is solid. Strange body and strange trajectory. Distortion. "
@@ -131,7 +131,7 @@ text_encoder = T5EncoderModel.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Euler": EulerDiscreteScheduler,
     "Euler A": EulerAncestralDiscreteScheduler,
     "DPM++": DPMSolverMultistepScheduler, 

examples/cogvideox_fun/predict_v2v.py

@@ -23,7 +23,7 @@ from videox_fun.pipeline import (CogVideoXFunInpaintPipeline,
 from videox_fun.utils import (apply_gpu_memory_mode, get_video_to_video_latent,
                               merge_lora, save_videos_grid, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -71,7 +71,7 @@ video_length        = 49
 fps                 = 8
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 # If you are preparing to redraw the reference video, set validation_video and validation_video_mask. 
 # If you do not use validation_video_mask, the entire video will be redrawn; 
@@ -138,7 +138,7 @@ text_encoder = T5EncoderModel.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Euler": EulerDiscreteScheduler,
     "Euler A": EulerAncestralDiscreteScheduler,
     "DPM++": DPMSolverMultistepScheduler, 

examples/cogvideox_fun/predict_v2v_control.py

@@ -23,7 +23,7 @@ from videox_fun.pipeline import CogVideoXFunControlPipeline
 from videox_fun.utils import (apply_gpu_memory_mode, get_video_to_video_latent,
                               merge_lora, save_videos_grid, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -71,7 +71,7 @@ video_length        = 49
 fps                 = 8
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 control_video           = "asset/pose.mp4"
 
@@ -132,7 +132,7 @@ text_encoder = T5EncoderModel.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Euler": EulerDiscreteScheduler,
     "Euler A": EulerAncestralDiscreteScheduler,
     "DPM++": DPMSolverMultistepScheduler, 

examples/ernie_image/predict_t2i.py

@@ -17,7 +17,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               FlowUniPCMultistepScheduler,
                               apply_gpu_memory_mode, merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -62,7 +62,7 @@ lora_path           = None
 sample_size         = [1728, 992]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 prompt              = "1girl, black_hair, brown_eyes, earrings, freckles, grey_background, jewelry, lips, long_hair, looking_at_viewer, nose, piercing, realistic, red_lips, solo, upper_body"
 negative_prompt     = "低分辨率，低画质，肢体畸形，手指畸形，画面过饱和，蜡像感，人脸无细节，过度光滑，画面具有AI感。构图混乱。文字模糊，扭曲。"
@@ -120,7 +120,7 @@ text_encoder = Mistral3Model.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/fantasytalking/predict_s2v.py

@@ -27,7 +27,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               merge_video_audio, save_videos_grid,
                               unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -106,7 +106,7 @@ video_length        = 81
 fps                 = 23
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 # If you want to generate from text, please set the validation_image_start = None
 validation_image_start  = "asset/8.png"
@@ -201,7 +201,7 @@ audio_encoder_path = model_name_audio if model_name_audio is not None else os.pa
 audio_encoder = FantasyTalkingAudioEncoder(audio_encoder_path)
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/flashhead/predict_s2v.py

@@ -22,7 +22,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               get_image_latent, merge_lora, merge_video_audio,
                               save_videos_grid, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -71,7 +71,7 @@ segment_frame_length    = 33
 fps                     = 25 
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 # The path of the reference image
 ref_image               = "asset/9.png"
@@ -140,7 +140,7 @@ audio_encoder = FlashHeadAudioEncoder(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/flux/predict_t2i.py

@@ -18,7 +18,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               FlowUniPCMultistepScheduler,
                               apply_gpu_memory_mode, merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -63,7 +63,7 @@ lora_path           = None
 sample_size         = [1344, 768]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 prompt              = "1girl, black_hair, brown_eyes, earrings, freckles, grey_background, jewelry, lips, long_hair, looking_at_viewer, nose, piercing, realistic, red_lips, solo, upper_body"
 negative_prompt     = " "
@@ -128,7 +128,7 @@ text_encoder_2 = T5EncoderModel.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/flux2/predict_t2i.py

@@ -18,7 +18,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               FlowUniPCMultistepScheduler,
                               apply_gpu_memory_mode, merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -63,7 +63,7 @@ lora_path           = None
 sample_size         = [1344, 768]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 # Please use as detailed a prompt as possible to describe the object that needs to be generated.
 prompt              = "1girl, black_hair, brown_eyes, earrings, freckles, grey_background, jewelry, lips, long_hair, looking_at_viewer, nose, piercing, realistic, red_lips, solo, upper_body"
@@ -123,7 +123,7 @@ text_encoder = Mistral3ForConditionalGeneration.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/flux2_fun/predict_i2i_inpaint.py

@@ -21,7 +21,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image,
                               get_image_latent, merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -68,7 +68,7 @@ lora_path           = None
 sample_size         = [1728, 992]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 image               = None 
 control_image       = None
@@ -136,7 +136,7 @@ text_encoder = Mistral3ForConditionalGeneration.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/flux2_fun/predict_t2i_control.py

@@ -21,7 +21,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image,
                               get_image_latent, merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -68,7 +68,7 @@ lora_path           = None
 sample_size         = [1728, 992]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 image               = None
 control_image       = "asset/pose.jpg"
@@ -136,7 +136,7 @@ text_encoder = Mistral3ForConditionalGeneration.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/flux2_fun/predict_t2i_control_ref.py

@@ -21,7 +21,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image,
                               get_image_latent, merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -68,7 +68,7 @@ lora_path           = None
 sample_size         = [1728, 992]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 image               = "asset/8.png"
 control_image       = "asset/pose.jpg"
@@ -136,7 +136,7 @@ text_encoder = Mistral3ForConditionalGeneration.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/hunyuanvideo/predict_i2v.py

@@ -24,7 +24,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image, merge_lora,
                               save_videos_grid, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -71,7 +71,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 # If you want to generate from text, please set the validation_image_start = None and validation_image_end = None
 validation_image_start  = "asset/1.png"
@@ -152,7 +152,7 @@ image_processor = CLIPImageProcessor.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/hunyuanvideo/predict_t2v.py

@@ -22,7 +22,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, merge_lora,
                               save_videos_grid, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -69,7 +69,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 prompt              = "1girl, black_hair, brown_eyes, earrings, freckles, grey_background, jewelry, lips, long_hair, looking_at_viewer, nose, piercing, realistic, red_lips, solo, upper_body"
 negative_prompt     = "The video is not of a high quality, it has a low resolution. Watermark present in each frame. The background is solid. Strange body and strange trajectory. Distortion. "
@@ -141,7 +141,7 @@ text_encoder_2 = CLIPTextModel.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/infinitetalk/predict_s2v.py

@@ -25,7 +25,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               get_image_latent, merge_lora, merge_video_audio,
                               save_videos_grid, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -89,7 +89,7 @@ segment_frame_length    = 81
 fps                     = 25
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 # The path of the reference image
 ref_image               = "asset/8.png"
@@ -180,7 +180,7 @@ clip_image_encoder = CLIPModel.from_pretrained(
 clip_image_encoder = clip_image_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/lens/predict_t2i.py

@@ -17,7 +17,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               FlowUniPCMultistepScheduler,
                               apply_gpu_memory_mode, merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -62,7 +62,7 @@ lora_path           = None
 sample_size         = [1728, 992]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 # Set to True on A100/V100 to dequantize MXFP4 GPT-OSS weights.
 dequantize_mxfp4    = False
@@ -132,7 +132,7 @@ text_encoder = LensGptOssEncoder.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/lingbot_video/predict_i2v.py

@@ -72,7 +72,7 @@ video_length        = 81
 fps                 = 24
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 # The condition image is used twice: as Qwen3-VL visual input and as a clean
 # first-frame latent injected into the diffusion latent (ti2v).
@@ -155,7 +155,7 @@ text_encoder = Qwen3VLForConditionalGeneration.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow_Unipc": FlowUniPCMultistepScheduler,
 }[sampler_name]
 scheduler = Chosen_Scheduler.from_pretrained(

examples/lingbot_video/predict_t2v.py

@@ -73,7 +73,7 @@ video_length        = 81
 fps                 = 24
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 # prompts
 # Write a plain natural-language prompt: it is ALWAYS rewritten into the
@@ -157,7 +157,7 @@ text_encoder = Qwen3VLForConditionalGeneration.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow_Unipc": FlowUniPCMultistepScheduler,
 }[sampler_name]
 scheduler = Chosen_Scheduler.from_pretrained(

examples/lingbot_video/predict_t2v_refine.py

@@ -84,7 +84,7 @@ video_length        = 81
 fps                 = 24
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 # prompts
 # Write a plain natural-language prompt: it is ALWAYS rewritten into the
@@ -187,7 +187,7 @@ processor = AutoProcessor.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow_Unipc": FlowUniPCMultistepScheduler,
 }[sampler_name]
 scheduler = Chosen_Scheduler.from_pretrained(

examples/lingbot_world/predict_i2v.py

@@ -92,7 +92,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 
 # Camera trajectory (poses.npy / intrinsics.npy) + reference image + prompt.
@@ -191,7 +191,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/lingbot_world/predict_i2v_fast.py

@@ -108,7 +108,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 
 # Camera trajectory (poses.npy / intrinsics.npy) + reference image + prompt.
@@ -190,7 +190,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/longcatvideo/predict_i2v.py

@@ -21,7 +21,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image_to_video_latent,
                               merge_lora, save_videos_grid, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -68,7 +68,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 validation_image_start  = "asset/1.png"
 
@@ -131,7 +131,7 @@ text_encoder = UMT5EncoderModel.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/longcatvideo/predict_s2v_avatar.py

@@ -25,7 +25,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               merge_lora, merge_video_audio, save_videos_grid,
                               unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -80,7 +80,7 @@ audio_path = "asset/talk.wav"
 use_audio_vocal_separator = False
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 # Prompt
 prompt              = "A young woman with long flowing purple hair stands by the seaside on a sunny day, singing. Wearing a white sleeveless dress with a navy blue bow at the collar, her hair gently sways in the ocean breeze. The sparkling sea, blue sky with white clouds, and pink wildflowers along the shore create a beautiful and vibrant scene."
@@ -147,7 +147,7 @@ audio_encoder = LongCatVideoAudioEncoder(
 audio_encoder.audio_encoder.feature_extractor._freeze_parameters()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/longcatvideo/predict_t2v.py

@@ -21,7 +21,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, merge_lora,
                               save_videos_grid, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -68,7 +68,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 # Prompt
 prompt              = "1girl, black_hair, brown_eyes, earrings, freckles, grey_background, jewelry, lips, long_hair, looking_at_viewer, nose, piercing, realistic, red_lips, solo, upper_body"
@@ -129,7 +129,7 @@ text_encoder = UMT5EncoderModel.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/ltx2.3/predict_i2v.py

@@ -22,7 +22,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, merge_lora,
                               save_videos_with_audio_grid, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -68,7 +68,7 @@ video_length        = 121
 fps                 = 24
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 # If you want to generate from text, please set the validation_image_start = None and validation_image_end = None
 validation_image_start  = "asset/1.png"

examples/ltx2.3/predict_t2v.py

@@ -22,7 +22,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, merge_lora,
                               save_videos_with_audio_grid, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -68,7 +68,7 @@ video_length        = 121
 fps                 = 24
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 prompt              = "A brown dog barks on a sofa, sitting on a light-colored couch in a cozy room. Behind the dog, there is a framed painting on a shelf, surrounded by pink flowers. "
 negative_prompt     = "worst quality, inconsistent motion, blurry, jittery, distorted, static, low quality, artifacts"

examples/ltx2/predict_i2v.py

@@ -22,7 +22,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, merge_lora,
                               save_videos_with_audio_grid, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -68,7 +68,7 @@ video_length        = 121
 fps                 = 24
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 # If you want to generate from text, please set the validation_image_start = None and validation_image_end = None
 validation_image_start  = "asset/1.png"

examples/ltx2/predict_i2v_upsample.py

@@ -23,7 +23,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, merge_lora,
                               save_videos_with_audio_grid, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -72,7 +72,7 @@ fps                 = 24
 enable_latent_upsample = True
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 # If you want to generate from text, please set the validation_image_start = None and validation_image_end = None
 validation_image_start  = "asset/1.png"

examples/ltx2/predict_t2v.py

@@ -22,7 +22,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, merge_lora,
                               save_videos_with_audio_grid, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -68,7 +68,7 @@ video_length        = 121
 fps                 = 24
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 prompt              = "A brown dog barks on a sofa, sitting on a light-colored couch in a cozy room. Behind the dog, there is a framed painting on a shelf, surrounded by pink flowers. "
 negative_prompt     = "worst quality, inconsistent motion, blurry, jittery, distorted, static, low quality, artifacts"

examples/minimax_h3/predict_i2v.py

@@ -80,7 +80,7 @@ validation_image_start  = "asset/1.png"
 validation_image_end    = None
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 prompt              = "一只棕色的狗摇着头，坐在舒适房间里的浅色沙发上。在狗的后面，架子上有一幅镶框的画，周围是粉红色的花朵。房间里柔和温暖的灯光营造出舒适的氛围。"
 seed                = 43

examples/minimax_h3_fun/predict_v2v_control.py

@@ -86,7 +86,7 @@ fps                 = 24
 control_context_scale = 1.00
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 control_video       = "asset/pose.mp4"
 # Inpaint inputs, only read by checkpoints trained with `--enable_inpaint` (control_in_dim widened, e.g. 49):

examples/minimax_h3_fun/predict_v2v_control_inpaint.py

@@ -86,7 +86,7 @@ fps                 = 24
 control_context_scale = 1.00
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 # Path of the control (e.g. pose) video; leaving it None zeroes the control channels of the side branch. With
 # inpaint inputs given the mask then guides the run on its own (the layout training reaches when it drops the

examples/mova/predict_i2v.py

@@ -22,7 +22,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, merge_lora,
                               save_videos_with_audio_grid, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,

examples/phantom/predict_s2v.py

@@ -103,7 +103,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 subject_ref_images      = ["asset/ref_1.png", "asset/ref_2.png"]
 
@@ -177,7 +177,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/qwenimage/predict_i2i_layered.py

@@ -21,7 +21,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               FlowUniPCMultistepScheduler,
                               apply_gpu_memory_mode, merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -81,7 +81,7 @@ lora_path           = None
 resolution          = 640
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 image               = "asset/yarn-art-pikachu.png"
 
@@ -148,7 +148,7 @@ processor = Qwen2VLProcessor.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/qwenimage/predict_t2i.py

@@ -19,7 +19,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               FlowUniPCMultistepScheduler,
                               apply_gpu_memory_mode, merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -79,7 +79,7 @@ lora_path           = None
 sample_size         = [1344, 768]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 # Please use as detailed a prompt as possible to describe the object that needs to be generated.
 prompt              = "1girl, black_hair, brown_eyes, earrings, freckles, grey_background, jewelry, lips, long_hair, looking_at_viewer, nose, piercing, realistic, red_lips, solo, upper_body"
@@ -138,7 +138,7 @@ text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/qwenimage/predict_t2i_edit.py

@@ -21,7 +21,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image, merge_lora,
                               unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -81,7 +81,7 @@ lora_path           = None
 sample_size         = [1344, 768]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 image               = "asset/8.png"
 # Please use as detailed a prompt as possible to describe the object that needs to be generated.
@@ -147,7 +147,7 @@ processor = Qwen2VLProcessor.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/qwenimage/predict_t2i_edit_plus.py

@@ -21,7 +21,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image, merge_lora,
                               unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -81,7 +81,7 @@ lora_path           = None
 sample_size         = [1344, 768]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 image               = ["asset/8.png", "asset/ref_1.png"]
 # Please use as detailed a prompt as possible to describe the object that needs to be generated.
@@ -147,7 +147,7 @@ processor = Qwen2VLProcessor.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/qwenimage_fun/predict_i2i_inpaint.py

@@ -22,7 +22,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image_latent,
                               merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -84,7 +84,7 @@ lora_path           = None
 sample_size         = [1728, 992]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 control_image       = "asset/pose.jpg"
 inpaint_image       = "asset/8.png"
@@ -151,7 +151,7 @@ text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/qwenimage_fun/predict_t2i_control.py

@@ -22,7 +22,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image_latent,
                               merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -84,7 +84,7 @@ lora_path           = None
 sample_size         = [1728, 992]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 control_image       = "asset/pose.jpg"
 inpaint_image       = None
@@ -151,7 +151,7 @@ text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/qwenimage_instantx/predict_t2i_control.py

@@ -22,7 +22,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image, merge_lora,
                               unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -85,7 +85,7 @@ lora_path           = None
 sample_size         = [1728, 992]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 control_image       = "asset/pose.jpg"
 controlnet_conditioning_scale = 0.80
@@ -166,7 +166,7 @@ text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/turbodiffusion/predict_i2v_wan2.2.py

@@ -85,7 +85,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 # If you want to generate from text, please set the validation_image_start = None and validation_image_end = None
 validation_image_start  = "asset/1.png"
@@ -192,7 +192,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/turbodiffusion/predict_t2v_wan2.1.py

@@ -81,7 +81,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 prompt              = "A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about."
 negative_prompt     = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
@@ -152,7 +152,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.1/predict_i2v.py

@@ -103,7 +103,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 # If you want to generate from text, please set the validation_image_start = None and validation_image_end = None
 validation_image_start  = "asset/1.png"
@@ -178,7 +178,7 @@ clip_image_encoder = CLIPModel.from_pretrained(
 clip_image_encoder = clip_image_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.1/predict_i2v_tae.py

@@ -114,7 +114,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 # If you want to generate from text, please set the validation_image_start = None and validation_image_end = None
 validation_image_start  = "asset/1.png"
@@ -203,7 +203,7 @@ clip_image_encoder = CLIPModel.from_pretrained(
 clip_image_encoder = clip_image_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.1/predict_t2v.py

@@ -101,7 +101,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 prompt              = "一只棕色的狗摇着头，坐在舒适房间里的浅色沙发上。在狗的后面，架子上有一幅镶框的画，周围是粉红色的花朵。房间里柔和温暖的灯光营造出舒适的氛围。"
 negative_prompt     = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
@@ -165,7 +165,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.1/predict_t2v_tae.py

@@ -112,7 +112,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 prompt              = "一只棕色的狗摇着头，坐在舒适房间里的浅色沙发上。在狗的后面，架子上有一幅镶框的画，周围是粉红色的花朵。房间里柔和温暖的灯光营造出舒适的氛围。"
 negative_prompt     = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
@@ -190,7 +190,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.1_causal_forcing/predict_t2v.py

@@ -211,7 +211,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.1_causal_forcing/predict_t2v_stream.py

@@ -224,7 +224,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.1_flex_forcing/predict_t2v.py

@@ -207,7 +207,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.1_flex_forcing/predict_t2v_edit.py

@@ -211,7 +211,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.1_fun/predict_i2v.py

@@ -103,7 +103,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 # If you want to generate from text, please set the validation_image_start = None and validation_image_end = None
 validation_image_start  = "asset/1.png"
@@ -179,7 +179,7 @@ clip_image_encoder = CLIPModel.from_pretrained(
 clip_image_encoder = clip_image_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.1_fun/predict_t2v.py

@@ -103,7 +103,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 prompt              = "一只棕色的狗摇着头，坐在舒适房间里的浅色沙发上。在狗的后面，架子上有一幅镶框的画，周围是粉红色的花朵。房间里柔和温暖的灯光营造出舒适的氛围。"
 negative_prompt     = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
@@ -178,7 +178,7 @@ else:
     clip_image_processor = None
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.1_fun/predict_v2v_control.py

@@ -104,7 +104,7 @@ video_length        = 49
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 control_video           = "asset/pose.mp4"
 control_camera_txt      = None
@@ -187,7 +187,7 @@ clip_image_encoder = CLIPModel.from_pretrained(
 clip_image_encoder = clip_image_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.1_fun/predict_v2v_control_camera.py

@@ -104,7 +104,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 control_video           = None
 control_camera_txt      = "asset/Pan_Left.txt"
@@ -187,7 +187,7 @@ clip_image_encoder = CLIPModel.from_pretrained(
 clip_image_encoder = clip_image_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.1_fun/predict_v2v_control_ref.py

@@ -104,7 +104,7 @@ video_length        = 49
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 control_video           = "asset/pose.mp4"
 control_camera_txt      = None
@@ -187,7 +187,7 @@ clip_image_encoder = CLIPModel.from_pretrained(
 clip_image_encoder = clip_image_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.1_self_forcing/predict_t2v.py

@@ -84,7 +84,7 @@ independent_first_frame = False
 context_noise           = 0.0
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 prompt              = "A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about."
 negative_prompt     = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
@@ -157,7 +157,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.1_self_forcing/predict_t2v_forcing_kv.py

@@ -109,7 +109,7 @@ forcing_kv_num_frame_patch         = 6    # token segments per latent frame
 forcing_kv_sim_retention_ratio     = 0.33 # fraction of candidate segments kept
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 prompts = [
     "A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about.",
@@ -187,7 +187,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.1_self_forcing/predict_t2v_stream.py

@@ -97,7 +97,7 @@ streaming               = True
 save_mode               = "segments"
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 prompt              = "A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about."
 negative_prompt     = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
@@ -170,7 +170,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.1_vace/predict_i2v.py

@@ -101,7 +101,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 control_video           = None
 start_image             = "asset/1.png"
@@ -179,7 +179,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.1_vace/predict_s2v.py

@@ -102,7 +102,7 @@ fps                 = 16
 vace_context_scale  = 1.00
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 control_video           = None
 start_image             = None
@@ -179,7 +179,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.1_vace/predict_v2v_control.py

@@ -102,7 +102,7 @@ fps                 = 16
 vace_context_scale  = 1.00
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 control_video           = "asset/pose.mp4"
 start_image             = None
@@ -179,7 +179,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2/predict_animate.py

@@ -112,7 +112,7 @@ segment_frame_length    = 77
 fps                     = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 prompt              = "视频中的人在做动作"
 negative_prompt     = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
@@ -212,7 +212,7 @@ clip_image_encoder = CLIPModel.from_pretrained(
 clip_image_encoder = clip_image_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2/predict_i2v.py

@@ -105,7 +105,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 # If you want to generate from text, please set the validation_image_start = None and validation_image_end = None
 validation_image_start  = "asset/1.png"
@@ -203,7 +203,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2/predict_s2v.py

@@ -107,7 +107,7 @@ segment_frame_length    = 80
 fps                     = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 # The path of the pose control video
 control_video           = "asset/pose.mp4"  
@@ -216,7 +216,7 @@ audio_encoder = WanAudioEncoder(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2/predict_t2v.py

@@ -104,7 +104,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 prompt              = "一只棕色的狗摇着头，坐在舒适房间里的浅色沙发上。在狗的后面，架子上有一幅镶框的画，周围是粉红色的花朵。房间里柔和温暖的灯光营造出舒适的氛围。"
 negative_prompt     = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
@@ -192,7 +192,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
     torch_dtype=weight_dtype)
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2/predict_ti2v.py

@@ -107,7 +107,7 @@ video_length        = 121
 fps                 = 24
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 # If you want to generate from text, please set the validation_image_start = None and validation_image_end = None
 validation_image_start  = "asset/1.png"
@@ -204,7 +204,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2/predict_ti2v_tae.py

@@ -111,7 +111,7 @@ video_length        = 81
 fps                 = 24
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 # If you want to generate from text, please set the validation_image_start = None and validation_image_end = None
 validation_image_start  = "asset/1.png"
@@ -221,7 +221,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2_fun/predict_i2v.py

@@ -106,7 +106,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 # If you want to generate from text, please set the validation_image_start = None and validation_image_end = None
 validation_image_start  = "asset/1.png"
@@ -206,7 +206,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2_fun/predict_i2v_2.2vae.py

@@ -109,7 +109,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 # If you want to generate from text, please set the validation_image_start = None and validation_image_end = None
 validation_image_start  = "asset/1.png"
@@ -209,7 +209,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2_fun/predict_i2v_2.2vae_tae.py

@@ -113,7 +113,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 # If you want to generate from text, please set the validation_image_start = None and validation_image_end = None
 validation_image_start  = "asset/1.png"
@@ -224,7 +224,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2_fun/predict_i2v_5b.py

@@ -108,7 +108,7 @@ video_length        = 121
 fps                 = 24
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 # If you want to generate from text, please set the validation_image_start = None and validation_image_end = None
 validation_image_start  = "asset/1.png"
@@ -208,7 +208,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2_fun/predict_t2v.py

@@ -106,7 +106,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 # 使用更长的neg prompt如"模糊，突变，变形，失真，画面暗，文本字幕，画面固定，连环画，漫画，线稿，没有主体。"，可以增加稳定性
 # 在neg prompt中添加"安静，固定"等词语可以增加动态性。
@@ -202,7 +202,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2_fun/predict_t2v_2.2vae.py

@@ -107,7 +107,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 # 使用更长的neg prompt如"模糊，突变，变形，失真，画面暗，文本字幕，画面固定，连环画，漫画，线稿，没有主体。"，可以增加稳定性
 # 在neg prompt中添加"安静，固定"等词语可以增加动态性。
@@ -203,7 +203,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2_fun/predict_t2v_2.2vae_tae.py

@@ -112,7 +112,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 # 使用更长的neg prompt如"模糊，突变，变形，失真，画面暗，文本字幕，画面固定，连环画，漫画，线稿，没有主体。"，可以增加稳定性
 # 在neg prompt中添加"安静，固定"等词语可以增加动态性。
@@ -219,7 +219,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2_fun/predict_t2v_5b.py

@@ -106,7 +106,7 @@ video_length        = 121
 fps                 = 21
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 # 使用更长的neg prompt如"模糊，突变，变形，失真，画面暗，文本字幕，画面固定，连环画，漫画，线稿，没有主体。"，可以增加稳定性
 # 在neg prompt中添加"安静，固定"等词语可以增加动态性。
@@ -202,7 +202,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2_fun/predict_v2v_control.py

@@ -111,7 +111,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 control_video           = "asset/pose.mp4"
 control_camera_txt      = None
@@ -218,7 +218,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2_fun/predict_v2v_control_5b.py

@@ -111,7 +111,7 @@ video_length        = 121
 fps                 = 24
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 control_video           = "asset/pose.mp4"
 control_camera_txt      = None
@@ -218,7 +218,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2_fun/predict_v2v_control_camera.py

@@ -111,7 +111,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 control_video           = None
 control_camera_txt      = "asset/Zoom_In.txt"
@@ -218,7 +218,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2_fun/predict_v2v_control_camera_5b.py

@@ -111,7 +111,7 @@ video_length        = 81
 fps                 = 24
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 control_video           = None
 control_camera_txt      = "asset/Zoom_In.txt"
@@ -218,7 +218,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2_fun/predict_v2v_control_ref.py

@@ -111,7 +111,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 control_video           = "asset/pose.mp4"
 control_camera_txt      = None
@@ -218,7 +218,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2_fun/predict_v2v_control_ref_5b.py

@@ -111,7 +111,7 @@ video_length        = 121
 fps                 = 24
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype            = torch.bfloat16
 control_video           = "asset/pose.mp4"
 control_camera_txt      = None
@@ -218,7 +218,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2_vace_fun/predict_i2v.py

@@ -107,7 +107,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 control_video       = None
 start_image         = "asset/1.png"
@@ -221,7 +221,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2_vace_fun/predict_s2v.py

@@ -107,7 +107,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 control_video       = None
 start_image         = None
@@ -221,7 +221,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2_vace_fun/predict_v2v_control.py

@@ -107,7 +107,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 control_video       = "asset/pose.mp4"
 start_image         = None
@@ -221,7 +221,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2_vace_fun/predict_v2v_control_ref.py

@@ -107,7 +107,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 control_video       = "asset/pose.mp4"
 start_image         = None
@@ -221,7 +221,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/wan2.2_vace_fun/predict_v2v_mask.py

@@ -107,7 +107,7 @@ video_length        = 81
 fps                 = 16
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 control_video       = None
 start_image         = None
@@ -221,7 +221,7 @@ text_encoder = WanT5EncoderModel.from_pretrained(
 text_encoder = text_encoder.eval()
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/z_image/predict_t2i.py

@@ -17,7 +17,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               FlowUniPCMultistepScheduler,
                               apply_gpu_memory_mode, merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -62,7 +62,7 @@ lora_path           = None
 sample_size         = [1728, 992]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 # Please use as detailed a prompt as possible to describe the object that needs to be generated.
 prompt              = "一位年轻女子站在阳光明媚的海岸线上，白裙在轻拂的海风中微微飘动。她拥有一头鲜艳的紫色长发，在风中轻盈舞动，发间系着一个精致的黑色蝴蝶结，与身后柔和的蔚蓝天空形成鲜明对比。她面容清秀，眉目精致，透着一股甜美的青春气息；神情柔和，略带羞涩，目光静静地凝望着远方的地平线，双手自然交叠于身前，仿佛沉浸在思绪之中。在她身后，是辽阔无垠、波光粼粼的大海，阳光洒在海面上，映出温暖的金色光晕。"
@@ -122,7 +122,7 @@ text_encoder = Qwen3ForCausalLM.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/z_image/predict_turbo_t2i.py

@@ -17,7 +17,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               FlowUniPCMultistepScheduler,
                               apply_gpu_memory_mode, merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -62,7 +62,7 @@ lora_path           = None
 sample_size         = [1728, 992]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 # Please use as detailed a prompt as possible to describe the object that needs to be generated.
 prompt              = "一位年轻女子站在阳光明媚的海岸线上，白裙在轻拂的海风中微微飘动。她拥有一头鲜艳的紫色长发，在风中轻盈舞动，发间系着一个精致的黑色蝴蝶结，与身后柔和的蔚蓝天空形成鲜明对比。她面容清秀，眉目精致，透着一股甜美的青春气息；神情柔和，略带羞涩，目光静静地凝望着远方的地平线，双手自然交叠于身前，仿佛沉浸在思绪之中。在她身后，是辽阔无垠、波光粼粼的大海，阳光洒在海面上，映出温暖的金色光晕。"
@@ -122,7 +122,7 @@ text_encoder = Qwen3ForCausalLM.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/z_image_fun/predict_i2i_inpaint_2.1.py

@@ -19,7 +19,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image_latent,
                               merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -66,7 +66,7 @@ lora_path           = None
 sample_size         = [1728, 992]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 control_image       = "asset/pose.jpg"
 inpaint_image       = "asset/8.png"
@@ -133,7 +133,7 @@ text_encoder = Qwen3ForCausalLM.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/z_image_fun/predict_i2i_inpaint_2.1_lite.py

@@ -19,7 +19,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image_latent,
                               merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -66,7 +66,7 @@ lora_path           = None
 sample_size         = [1728, 992]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 control_image       = "asset/pose.jpg"
 inpaint_image       = "asset/8.png"
@@ -133,7 +133,7 @@ text_encoder = Qwen3ForCausalLM.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/z_image_fun/predict_i2i_tile_2.1.py

@@ -19,7 +19,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image_latent,
                               merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -66,7 +66,7 @@ lora_path           = None
 sample_size         = [2048, 2048]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 control_image       = "asset/low_res.png"
 # The inpaint_image and mask_image is useless in tile model, just set them to None.
@@ -134,7 +134,7 @@ text_encoder = Qwen3ForCausalLM.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/z_image_fun/predict_i2i_tile_2.1_lite.py

@@ -19,7 +19,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image_latent,
                               merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -66,7 +66,7 @@ lora_path           = None
 sample_size         = [2048, 2048]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 control_image       = "asset/low_res.png"
 # The inpaint_image and mask_image is useless in tile model, just set them to None.
@@ -134,7 +134,7 @@ text_encoder = Qwen3ForCausalLM.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/z_image_fun/predict_t2i_control_2.1.py

@@ -21,7 +21,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image_latent,
                               merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -68,7 +68,7 @@ lora_path           = None
 sample_size         = [1728, 992]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 control_image       = "asset/pose.jpg"
 inpaint_image       = None
@@ -141,7 +141,7 @@ text_encoder = Qwen3ForCausalLM.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/z_image_fun/predict_t2i_control_2.1_lite.py

@@ -21,7 +21,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image_latent,
                               merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -68,7 +68,7 @@ lora_path           = None
 sample_size         = [1728, 992]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 control_image       = "asset/pose.jpg"
 inpaint_image       = None
@@ -141,7 +141,7 @@ text_encoder = Qwen3ForCausalLM.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/z_image_fun/predict_turbo_i2i_inpaint_2.0.py

@@ -19,7 +19,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image_latent,
                               merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -66,7 +66,7 @@ lora_path           = None
 sample_size         = [1728, 992]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 control_image       = "asset/pose.jpg"
 inpaint_image       = "asset/8.png"
@@ -133,7 +133,7 @@ text_encoder = Qwen3ForCausalLM.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/z_image_fun/predict_turbo_i2i_inpaint_2.1.py

@@ -19,7 +19,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image_latent,
                               merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -66,7 +66,7 @@ lora_path           = None
 sample_size         = [1728, 992]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 control_image       = "asset/pose.jpg"
 inpaint_image       = "asset/8.png"
@@ -133,7 +133,7 @@ text_encoder = Qwen3ForCausalLM.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/z_image_fun/predict_turbo_i2i_inpaint_2.1_lite.py

@@ -19,7 +19,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image_latent,
                               merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -66,7 +66,7 @@ lora_path           = None
 sample_size         = [1728, 992]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 control_image       = "asset/pose.jpg"
 inpaint_image       = "asset/8.png"
@@ -133,7 +133,7 @@ text_encoder = Qwen3ForCausalLM.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/z_image_fun/predict_turbo_i2i_tile_2.1.py

@@ -19,7 +19,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image_latent,
                               merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -66,7 +66,7 @@ lora_path           = None
 sample_size         = [1328, 1328]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 control_image       = "asset/low_res.png"
 # The inpaint_image and mask_image is useless in tile model, just set them to None.
@@ -134,7 +134,7 @@ text_encoder = Qwen3ForCausalLM.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,

examples/z_image_fun/predict_turbo_i2i_tile_2.1_lite.py

@@ -19,7 +19,7 @@ from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                               apply_gpu_memory_mode, get_image_latent,
                               merge_lora, unmerge_lora)
 
-# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
+# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
 # model_full_load means that the entire model will be moved to the GPU.
 # 
 # model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
@@ -66,7 +66,7 @@ lora_path           = None
 sample_size         = [1328, 1328]
 
 # Use torch.float16 if GPU does not support torch.bfloat16
-# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
+# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
 weight_dtype        = torch.bfloat16
 control_image       = "asset/low_res.png"
 # The inpaint_image and mask_image is useless in tile model, just set them to None.
@@ -134,7 +134,7 @@ text_encoder = Qwen3ForCausalLM.from_pretrained(
 )
 
 # Get Scheduler
-Chosen_Scheduler = scheduler_dict = {
+Chosen_Scheduler = {
     "Flow": FlowMatchEulerDiscreteScheduler,
     "Flow_Unipc": FlowUniPCMultistepScheduler,
     "Flow_DPM++": FlowDPMSolverMultistepScheduler,