diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-I2AV-TwoStage.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-I2AV-TwoStage.py new file mode 100644 index 0000000000000000000000000000000000000000..d23bd619db7f223eef9fdc16df52c43b54734314 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-I2AV-TwoStage.py @@ -0,0 +1,71 @@ +import torch +from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig +from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2 +from PIL import Image +from modelscope import dataset_snapshot_download + +vram_config = { + "offload_dtype": torch.float8_e5m2, + "offload_device": "cpu", + "onload_dtype": torch.float8_e5m2, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e5m2, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = LTX2AudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config), + ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-dev.safetensors", **vram_config), + ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-spatial-upscaler-x2-1.0.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"), + stage2_lora_config=ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-distilled-lora-384.safetensors"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) + +prompt = "A girl is very happy, she is speaking: “I enjoy working with Diffsynth-Studio, it's a perfect framework.”" +negative_prompt = ( + "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, " + "grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, " + "deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, " + "wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of " + "field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent " + "lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny " + "valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, " + "mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, " + "off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward " + "pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, " + "inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts." +) +height, width, num_frames = 512 * 2, 768 * 2, 121 +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/ltx-2/first_frame.jpg"] +) +image = Image.open("data/examples/ltx-2/first_frame.jpg").convert("RGB").resize((width, height)) +# first frame +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + seed=42, + height=height, + width=width, + num_frames=num_frames, + tiled=True, + use_two_stage_pipeline=True, + input_images=[image], + input_images_indexes=[0], + input_images_strength=1.0, +) +write_video_audio_ltx2( + video=video, + audio=audio, + output_path='ltx2.3_twostage_i2av_first.mp4', + fps=24, + audio_sample_rate=pipe.audio_vocoder.output_sampling_rate, +) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-DistilledPipeline.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-DistilledPipeline.py new file mode 100644 index 0000000000000000000000000000000000000000..8d67de9313835bddec66e2be8f16baed7f68c557 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-DistilledPipeline.py @@ -0,0 +1,58 @@ +import torch +from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig +from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2 + +vram_config = { + "offload_dtype": torch.float8_e5m2, + "offload_device": "cpu", + "onload_dtype": torch.float8_e5m2, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e5m2, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = LTX2AudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config), + ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-distilled.safetensors", **vram_config), + ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-spatial-upscaler-x2-1.0.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) + +prompt = "A girl is very happy, she is speaking: “I enjoy working with Diffsynth-Studio, it's a perfect framework.”" +negative_prompt = ( + "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, " + "grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, " + "deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, " + "wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of " + "field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent " + "lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny " + "valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, " + "mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, " + "off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward " + "pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, " + "inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts." +) +height, width, num_frames = 512 * 2, 768 * 2, 121 +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + seed=43, + height=height, + width=width, + num_frames=num_frames, + tiled=True, + use_distilled_pipeline=True, +) +write_video_audio_ltx2( + video=video, + audio=audio, + output_path='ltx2.3_distilled.mp4', + fps=24, + audio_sample_rate=pipe.audio_vocoder.output_sampling_rate, +) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-IC-LoRA-Motion-Track-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-IC-LoRA-Motion-Track-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..094289a7a82ed5eea27075810ed76224e17a306d --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-IC-LoRA-Motion-Track-Control.py @@ -0,0 +1,72 @@ +import torch +from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig +from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2 +from modelscope import dataset_snapshot_download +from diffsynth.utils.data import VideoData + +vram_config = { + "offload_dtype": torch.float8_e5m2, + "offload_device": "cpu", + "onload_dtype": torch.float8_e5m2, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e5m2, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = LTX2AudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config), + ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-dev.safetensors", **vram_config), + ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-spatial-upscaler-x2-1.0.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"), + stage2_lora_config=ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-distilled-lora-384.safetensors"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) +pipe.load_lora(pipe.dit, ModelConfig(model_id="Lightricks/LTX-2.3-22b-IC-LoRA-Motion-Track-Control", origin_file_pattern="ltx-2.3-22b-ic-lora-motion-track-control-ref0.5.safetensors")) +dataset_snapshot_download("DiffSynth-Studio/example_video_dataset", allow_file_pattern="ltx2/*", local_dir="data/example_video_dataset") +prompt = "[VISUAL]:Two cute orange cats, wearing boxing gloves, stand on a boxing ring and fight each other. [SOUNDS]:the sound of two cats boxing" +negative_prompt = ( + "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, " + "grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, " + "deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, " + "wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of " + "field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent " + "lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny " + "valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, " + "mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, " + "off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward " + "pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, " + "inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts." +) +height, width, num_frames = 512 * 2, 768 * 2, 121 +ref_scale_factor = 2 +frame_rate = 24 +input_image = VideoData("data/example_video_dataset/ltx2/video1.mp4", height=height // ref_scale_factor // 2, width=width // ref_scale_factor // 2)[0] +input_video = VideoData("data/example_video_dataset/ltx2/spatial_tracker_v2.mp4", height=height // ref_scale_factor // 2, width=width // ref_scale_factor // 2).raw_data() +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + seed=43, + height=height, + width=width, + num_frames=num_frames, + frame_rate=frame_rate, + in_context_videos=[input_video], + in_context_downsample_factor=ref_scale_factor, + input_images=[input_image], + input_images_indexes=[0], + tiled=True, + use_two_stage_pipeline=True, + clear_lora_before_state_two=True, +) +write_video_audio_ltx2( + video=video, + audio=audio, + output_path='ltx2.3_ic_lora.mp4', + fps=frame_rate, + audio_sample_rate=pipe.audio_vocoder.output_sampling_rate, +) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-IC-LoRA-Union-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-IC-LoRA-Union-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..b015f06ca03740cf19ee8116ff1fbb889b6e7e17 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-IC-LoRA-Union-Control.py @@ -0,0 +1,69 @@ +import torch +from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig +from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2 +from modelscope import dataset_snapshot_download +from diffsynth.utils.data import VideoData + +vram_config = { + "offload_dtype": torch.float8_e5m2, + "offload_device": "cpu", + "onload_dtype": torch.float8_e5m2, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e5m2, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = LTX2AudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config), + ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-dev.safetensors", **vram_config), + ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-spatial-upscaler-x2-1.0.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"), + stage2_lora_config=ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-distilled-lora-384.safetensors"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) +pipe.load_lora(pipe.dit, ModelConfig(model_id="Lightricks/LTX-2.3-22b-IC-LoRA-Union-Control", origin_file_pattern="ltx-2.3-22b-ic-lora-union-control-ref0.5.safetensors")) +dataset_snapshot_download("DiffSynth-Studio/example_video_dataset", allow_file_pattern="ltx2/*", local_dir="data/example_video_dataset") +prompt = "[VISUAL]:Two cute orange cats, wearing boxing gloves, stand on a boxing ring and fight each other. [SOUNDS]:the sound of two cats boxing" +negative_prompt = ( + "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, " + "grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, " + "deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, " + "wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of " + "field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent " + "lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny " + "valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, " + "mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, " + "off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward " + "pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, " + "inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts." +) +height, width, num_frames = 512 * 2, 768 * 2, 121 +ref_scale_factor = 2 +frame_rate = 24 +input_video = VideoData("data/example_video_dataset/ltx2/depth_video.mp4", height=height // ref_scale_factor // 2, width=width // ref_scale_factor // 2).raw_data() +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + seed=43, + height=height, + width=width, + num_frames=num_frames, + frame_rate=frame_rate, + in_context_videos=[input_video], + in_context_downsample_factor=ref_scale_factor, + tiled=True, + use_two_stage_pipeline=True, + clear_lora_before_state_two=True, +) +write_video_audio_ltx2( + video=video, + audio=audio, + output_path='ltx2.3_ic_lora.mp4', + fps=frame_rate, + audio_sample_rate=pipe.audio_vocoder.output_sampling_rate, +) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-OneStage.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-OneStage.py new file mode 100644 index 0000000000000000000000000000000000000000..95fa5cab58b99ad5a9a93f4b23ecbeb105ac56dc --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-OneStage.py @@ -0,0 +1,43 @@ +import torch +from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig +from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2 + +vram_config = { + "offload_dtype": torch.float8_e5m2, + "offload_device": "cpu", + "onload_dtype": torch.float8_e5m2, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e5m2, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = LTX2AudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config), + ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-dev.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) +prompt = "A girl is very happy, she is speaking: “I enjoy working with Diffsynth-Studio, it's a perfect framework.”" +negative_prompt = "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts." +height, width, num_frames = 512, 768, 121 +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + seed=43, + height=height, + width=width, + num_frames=num_frames, + tiled=True, +) +write_video_audio_ltx2( + video=video, + audio=audio, + output_path='ltx2.3_onestage.mp4', + fps=24, + audio_sample_rate=pipe.audio_vocoder.output_sampling_rate, +) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-TwoStage-Retake.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-TwoStage-Retake.py new file mode 100644 index 0000000000000000000000000000000000000000..65a6ebfe59a0dcc69ec450f46fcef39b271e0a6e --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-TwoStage-Retake.py @@ -0,0 +1,78 @@ +import torch +from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig +from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2 +from diffsynth.utils.data.audio import read_audio +from modelscope import dataset_snapshot_download +from diffsynth.utils.data import VideoData + +vram_config = { + "offload_dtype": torch.float8_e5m2, + "offload_device": "cpu", + "onload_dtype": torch.float8_e5m2, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e5m2, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = LTX2AudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config), + ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-dev.safetensors", **vram_config), + ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-spatial-upscaler-x2-1.0.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"), + stage2_lora_config=ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-distilled-lora-384.safetensors"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) + +dataset_snapshot_download("DiffSynth-Studio/example_video_dataset", allow_file_pattern="ltx2/*", local_dir="data/example_video_dataset") +prompt = "A beautiful woman with a flower crown is singing happily under a blooming cherry tree. She sings: 'Mummy don't know daddy's getting hot. At the body shop'" +negative_prompt = ( + "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, " + "grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, " + "deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, " + "wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of " + "field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent " + "lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny " + "valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, " + "mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, " + "off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward " + "pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, " + "inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts." +) + +height, width, num_frames, frame_rate = 512 * 2, 768 * 2, 121, 24 +path = "data/example_video_dataset/ltx2/video2.mp4" +video = VideoData(path, height=height, width=width).raw_data()[:num_frames] +assert len(video) == num_frames, f"Input video has {len(video)} frames, but expected {num_frames} frames based on the specified num_frames argument." +duration = num_frames / frame_rate +audio, audio_sample_rate = read_audio(path) + +# Regenerate the video within time regions. You can specify different time regions for video frames and audio retake. +# retake regions are in seconds, and the example below retakes video frames in the time regions of [1s, 2s] and [3s, 4s], and retakes audio in the time regions of [0s, 1s] and [4s, 5s]. +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + retake_video=video, + retake_video_regions=[(1, 2), (3, 4)], + retake_audio=audio, + audio_sample_rate=audio_sample_rate, + retake_audio_regions=[(0, 1), (4, 5)], + seed=43, + height=height, + width=width, + num_frames=num_frames, + frame_rate=frame_rate, + tiled=True, + use_two_stage_pipeline=True, +) +write_video_audio_ltx2( + video=video, + audio=audio, + output_path='ltx2.3_twostage_retake.mp4', + fps=frame_rate, + audio_sample_rate=pipe.audio_vocoder.output_sampling_rate, +) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-TwoStage.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-TwoStage.py new file mode 100644 index 0000000000000000000000000000000000000000..a954b40e81f306815dd629dae0e035f240ba9521 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-TwoStage.py @@ -0,0 +1,58 @@ +import torch +from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig +from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2 + +vram_config = { + "offload_dtype": torch.float8_e5m2, + "offload_device": "cpu", + "onload_dtype": torch.float8_e5m2, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e5m2, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = LTX2AudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config), + ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-dev.safetensors", **vram_config), + ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-spatial-upscaler-x2-1.0.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"), + stage2_lora_config=ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-distilled-lora-384.safetensors"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) +prompt = "A girl is very happy, she is speaking: “I enjoy working with Diffsynth-Studio, it's a perfect framework.”" +negative_prompt = ( + "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, " + "grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, " + "deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, " + "wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of " + "field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent " + "lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny " + "valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, " + "mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, " + "off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward " + "pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, " + "inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts." +) +height, width, num_frames = 512 * 2, 768 * 2, 121 +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + seed=43, + height=height, + width=width, + num_frames=num_frames, + tiled=True, + use_two_stage_pipeline=True, +) +write_video_audio_ltx2( + video=video, + audio=audio, + output_path='ltx2.3_twostage.mp4', + fps=24, + audio_sample_rate=pipe.audio_vocoder.output_sampling_rate, +) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/full/JoyAI-Echo-T2AV-splited.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/full/JoyAI-Echo-T2AV-splited.sh new file mode 100644 index 0000000000000000000000000000000000000000..05212e04c0f18b781d000c45d75fc37023b61a7e --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/full/JoyAI-Echo-T2AV-splited.sh @@ -0,0 +1,37 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "ltx2/LTX-2.3-T2AV-splited/*" --local_dir ./data/diffsynth_example_dataset + +# Splited Training +accelerate launch examples/ltx2/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/ltx2/LTX-2.3-T2AV-splited \ + --dataset_metadata_path data/diffsynth_example_dataset/ltx2/LTX-2.3-T2AV-splited/metadata.csv \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio" \ + --height 512 \ + --width 768 \ + --num_frames 121 \ + --dataset_repeat 1 \ + --model_id_with_origin_paths "jd-opensource/JoyAI-Echo:JoyAI-Echo-release.safetensors,google/gemma-3-12b-it-qat-q4_0-unquantized:model-*.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/JoyAI-Echo-T2AV-full-splited-cache" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --task "sft:data_process" + +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/ltx2/model_training/train.py \ + --dataset_base_path ./models/train/JoyAI-Echo-T2AV-full-splited-cache \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio" \ + --height 512 \ + --width 768 \ + --num_frames 121 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "jd-opensource/JoyAI-Echo:JoyAI-Echo-release.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/JoyAI-Echo-T2AV-full" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --task "sft:train" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/full/LTX-2-T2AV-splited.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/full/LTX-2-T2AV-splited.sh new file mode 100644 index 0000000000000000000000000000000000000000..47ed269337e276ece8b74ab82e099d2f34d574bc --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/full/LTX-2-T2AV-splited.sh @@ -0,0 +1,37 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "ltx2/LTX-2-T2AV-splited/*" --local_dir ./data/diffsynth_example_dataset + +# Splited Training +accelerate launch examples/ltx2/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/ltx2/LTX-2-T2AV-splited \ + --dataset_metadata_path data/diffsynth_example_dataset/ltx2/LTX-2-T2AV-splited/metadata.csv \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio" \ + --height 512 \ + --width 768 \ + --num_frames 121 \ + --dataset_repeat 1 \ + --model_id_with_origin_paths "DiffSynth-Studio/LTX-2-Repackage:text_encoder_post_modules.safetensors,DiffSynth-Studio/LTX-2-Repackage:video_vae_encoder.safetensors,DiffSynth-Studio/LTX-2-Repackage:audio_vae_encoder.safetensors,google/gemma-3-12b-it-qat-q4_0-unquantized:model-*.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LTX2-T2AV-full-splited-cache" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --task "sft:data_process" + +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/ltx2/model_training/train.py \ + --dataset_base_path ./models/train/LTX2-T2AV-full-splited-cache \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio" \ + --height 512 \ + --width 768 \ + --num_frames 121 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "DiffSynth-Studio/LTX-2-Repackage:transformer.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LTX2-T2AV-full" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --task "sft:train" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/full/LTX-2.3-I2AV-splited.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/full/LTX-2.3-I2AV-splited.sh new file mode 100644 index 0000000000000000000000000000000000000000..b688b2eb70d2e33cdde494322f45fbc4d64efb72 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/full/LTX-2.3-I2AV-splited.sh @@ -0,0 +1,37 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "ltx2/LTX-2.3-I2AV-splited/*" --local_dir ./data/diffsynth_example_dataset + +# Splited Training +accelerate launch examples/ltx2/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/ltx2/LTX-2.3-I2AV-splited \ + --dataset_metadata_path data/diffsynth_example_dataset/ltx2/LTX-2.3-I2AV-splited/metadata.csv \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio,input_image" \ + --height 512 \ + --width 768 \ + --num_frames 121 \ + --dataset_repeat 1 \ + --model_id_with_origin_paths "DiffSynth-Studio/LTX-2.3-Repackage:text_encoder_post_modules.safetensors,DiffSynth-Studio/LTX-2.3-Repackage:video_vae_encoder.safetensors,DiffSynth-Studio/LTX-2.3-Repackage:audio_vae_encoder.safetensors,google/gemma-3-12b-it-qat-q4_0-unquantized:model-*.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LTX2.3-I2AV-full-splited-cache" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --task "sft:data_process" + +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/ltx2/model_training/train.py \ + --dataset_base_path ./models/train/LTX2.3-I2AV-full-splited-cache \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio,input_image" \ + --height 512 \ + --width 768 \ + --num_frames 121 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "DiffSynth-Studio/LTX-2.3-Repackage:transformer.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LTX2.3-I2AV-full" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --task "sft:train" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/full/LTX-2.3-T2AV-splited.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/full/LTX-2.3-T2AV-splited.sh new file mode 100644 index 0000000000000000000000000000000000000000..24028d70d1d5b270434857551450537d8cfca6a4 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/full/LTX-2.3-T2AV-splited.sh @@ -0,0 +1,37 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "ltx2/LTX-2.3-T2AV-splited/*" --local_dir ./data/diffsynth_example_dataset + +# Splited Training +accelerate launch examples/ltx2/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/ltx2/LTX-2.3-T2AV-splited \ + --dataset_metadata_path data/diffsynth_example_dataset/ltx2/LTX-2.3-T2AV-splited/metadata.csv \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio" \ + --height 512 \ + --width 768 \ + --num_frames 121 \ + --dataset_repeat 1 \ + --model_id_with_origin_paths "DiffSynth-Studio/LTX-2.3-Repackage:text_encoder_post_modules.safetensors,DiffSynth-Studio/LTX-2.3-Repackage:video_vae_encoder.safetensors,DiffSynth-Studio/LTX-2.3-Repackage:audio_vae_encoder.safetensors,google/gemma-3-12b-it-qat-q4_0-unquantized:model-*.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LTX2.3-T2AV-full-splited-cache" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --task "sft:data_process" + +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/ltx2/model_training/train.py \ + --dataset_base_path ./models/train/LTX2.3-T2AV-full-splited-cache \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio" \ + --height 512 \ + --width 768 \ + --num_frames 121 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "DiffSynth-Studio/LTX-2.3-Repackage:transformer.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LTX2.3-T2AV-full" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --task "sft:train" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/JoyAI-Echo-T2AV-splited.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/JoyAI-Echo-T2AV-splited.sh new file mode 100644 index 0000000000000000000000000000000000000000..5c92b2722718cc1b5a432615a8f4795087fa59f5 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/JoyAI-Echo-T2AV-splited.sh @@ -0,0 +1,41 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "ltx2/LTX-2.3-T2AV-splited/*" --local_dir ./data/diffsynth_example_dataset + +# Splited Training +accelerate launch examples/ltx2/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/ltx2/LTX-2.3-T2AV-splited \ + --dataset_metadata_path data/diffsynth_example_dataset/ltx2/LTX-2.3-T2AV-splited/metadata.csv \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio" \ + --height 512 \ + --width 768 \ + --num_frames 121 \ + --dataset_repeat 1 \ + --model_id_with_origin_paths "jd-opensource/JoyAI-Echo:JoyAI-Echo-release.safetensors,google/gemma-3-12b-it-qat-q4_0-unquantized:model-*.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/JoyAI-Echo-T2AV_lora-splited-cache" \ + --lora_base_model "dit" \ + --lora_target_modules "to_k,to_q,to_v,to_out.0" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --task "sft:data_process" + +accelerate launch examples/ltx2/model_training/train.py \ + --dataset_base_path ./models/train/JoyAI-Echo-T2AV_lora-splited-cache \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio" \ + --height 512 \ + --width 768 \ + --num_frames 121 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "jd-opensource/JoyAI-Echo:JoyAI-Echo-release.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/JoyAI-Echo-T2AV_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_k,to_q,to_v,to_out.0" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --task "sft:train" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/LTX-2-T2AV-IC-LoRA-splited.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/LTX-2-T2AV-IC-LoRA-splited.sh new file mode 100644 index 0000000000000000000000000000000000000000..fa4d23c662efe112d0f89ffe95595dd4acd17a19 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/LTX-2-T2AV-IC-LoRA-splited.sh @@ -0,0 +1,41 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "ltx2/LTX-2-T2AV-IC-LoRA-splited/*" --local_dir ./data/diffsynth_example_dataset + +# Splited Training +accelerate launch examples/ltx2/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/ltx2/LTX-2-T2AV-IC-LoRA-splited \ + --dataset_metadata_path data/diffsynth_example_dataset/ltx2/LTX-2-T2AV-IC-LoRA-splited/metadata.json \ + --data_file_keys "video,input_audio,in_context_videos" \ + --extra_inputs "input_audio,in_context_videos,in_context_downsample_factor,frame_rate" \ + --height 512 \ + --width 768 \ + --num_frames 81 \ + --dataset_repeat 1 \ + --model_id_with_origin_paths "DiffSynth-Studio/LTX-2-Repackage:text_encoder_post_modules.safetensors,DiffSynth-Studio/LTX-2-Repackage:video_vae_encoder.safetensors,DiffSynth-Studio/LTX-2-Repackage:audio_vae_encoder.safetensors,google/gemma-3-12b-it-qat-q4_0-unquantized:model-*.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LTX2-T2AV-IC-LoRA-splited-cache" \ + --lora_base_model "dit" \ + --lora_target_modules "to_k,to_q,to_v,to_out.0" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --task "sft:data_process" + +accelerate launch examples/ltx2/model_training/train.py \ + --dataset_base_path ./models/train/LTX2-T2AV-IC-LoRA-splited-cache \ + --data_file_keys "video,input_audio,in_context_videos" \ + --extra_inputs "input_audio,in_context_videos,in_context_downsample_factor,frame_rate" \ + --height 512 \ + --width 768 \ + --num_frames 81 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "DiffSynth-Studio/LTX-2-Repackage:transformer.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LTX2-T2AV-IC-LoRA" \ + --lora_base_model "dit" \ + --lora_target_modules "to_k,to_q,to_v,to_out.0" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --task "sft:train" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/LTX-2-T2AV-noaudio.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/LTX-2-T2AV-noaudio.sh new file mode 100644 index 0000000000000000000000000000000000000000..ca91f2640c45004da59036fd76fd325d89e5fcda --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/LTX-2-T2AV-noaudio.sh @@ -0,0 +1,58 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "ltx2/LTX-2-T2AV-noaudio/*" --local_dir ./data/diffsynth_example_dataset + +# single stage training +# accelerate launch examples/ltx2/model_training/train.py \ +# --dataset_base_path data/diffsynth_example_dataset/ltx2/LTX-2-T2AV-noaudio \ +# --dataset_metadata_path data/diffsynth_example_dataset/ltx2/LTX-2-T2AV-noaudio/metadata.csv \ +# --height 256 \ +# --width 384 \ +# --num_frames 25\ +# --dataset_repeat 100 \ +# --model_id_with_origin_paths "DiffSynth-Studio/LTX-2-Repackage:transformer.safetensors,DiffSynth-Studio/LTX-2-Repackage:text_encoder_post_modules.safetensors,DiffSynth-Studio/LTX-2-Repackage:video_vae_encoder.safetensors,DiffSynth-Studio/LTX-2-Repackage:audio_vae_encoder.safetensors,google/gemma-3-12b-it-qat-q4_0-unquantized:model-*.safetensors" \ +# --learning_rate 1e-4 \ +# --num_epochs 5 \ +# --remove_prefix_in_ckpt "pipe.dit." \ +# --output_path "./models/train/LTX2-T2AV-noaudio_lora" \ +# --lora_base_model "dit" \ +# --lora_target_modules "to_k,to_q,to_v,to_out.0" \ +# --lora_rank 32 \ +# --use_gradient_checkpointing \ +# --find_unused_parameters + + +# Splited Training +accelerate launch examples/ltx2/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/ltx2/LTX-2-T2AV-noaudio \ + --dataset_metadata_path data/diffsynth_example_dataset/ltx2/LTX-2-T2AV-noaudio/metadata.csv \ + --height 512 \ + --width 768 \ + --num_frames 121\ + --dataset_repeat 1 \ + --model_id_with_origin_paths "DiffSynth-Studio/LTX-2-Repackage:text_encoder_post_modules.safetensors,DiffSynth-Studio/LTX-2-Repackage:video_vae_encoder.safetensors,DiffSynth-Studio/LTX-2-Repackage:audio_vae_encoder.safetensors,google/gemma-3-12b-it-qat-q4_0-unquantized:model-*.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LTX2-T2AV-noaudio_lora-splited-cache" \ + --lora_base_model "dit" \ + --lora_target_modules "to_k,to_q,to_v,to_out.0" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --task "sft:data_process" + + +accelerate launch examples/ltx2/model_training/train.py \ + --dataset_base_path ./models/train/LTX2-T2AV-noaudio_lora-splited-cache \ + --height 512 \ + --width 768 \ + --num_frames 121\ + --dataset_repeat 100 \ + --model_id_with_origin_paths "DiffSynth-Studio/LTX-2-Repackage:transformer.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LTX2-T2AV-noaudio_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_k,to_q,to_v,to_out.0" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --task "sft:train" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/LTX-2-T2AV-splited.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/LTX-2-T2AV-splited.sh new file mode 100644 index 0000000000000000000000000000000000000000..eefc17b24b03c24df2f0ddec63d181cac5322133 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/LTX-2-T2AV-splited.sh @@ -0,0 +1,62 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "ltx2/LTX-2-T2AV-splited/*" --local_dir ./data/diffsynth_example_dataset + +# Single Stage Training not recommended for T2AV due to the large memory consumption. Please use the Splited Training instead. +# accelerate launch examples/ltx2/model_training/train.py \ +# --dataset_base_path data/diffsynth_example_dataset/ltx2/LTX-2-T2AV-splited \ +# --dataset_metadata_path data/diffsynth_example_dataset/ltx2/LTX-2-T2AV-splited/metadata.csv \ +# --data_file_keys "video,input_audio" \ +# --extra_inputs "input_audio" \ +# --height 256 \ +# --width 384 \ +# --num_frames 25\ +# --dataset_repeat 100 \ +# --model_id_with_origin_paths "DiffSynth-Studio/LTX-2-Repackage:transformer.safetensors,DiffSynth-Studio/LTX-2-Repackage:text_encoder_post_modules.safetensors,DiffSynth-Studio/LTX-2-Repackage:video_vae_encoder.safetensors,DiffSynth-Studio/LTX-2-Repackage:audio_vae_encoder.safetensors,google/gemma-3-12b-it-qat-q4_0-unquantized:model-*.safetensors" \ +# --learning_rate 1e-4 \ +# --num_epochs 5 \ +# --remove_prefix_in_ckpt "pipe.dit." \ +# --output_path "./models/train/LTX2-T2AV_lora" \ +# --lora_base_model "dit" \ +# --lora_target_modules "to_k,to_q,to_v,to_out.0" \ +# --lora_rank 32 \ +# --use_gradient_checkpointing \ +# --find_unused_parameters + +# Splited Training +accelerate launch examples/ltx2/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/ltx2/LTX-2-T2AV-splited \ + --dataset_metadata_path data/diffsynth_example_dataset/ltx2/LTX-2-T2AV-splited/metadata.csv \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio" \ + --height 512 \ + --width 768 \ + --num_frames 121 \ + --dataset_repeat 1 \ + --model_id_with_origin_paths "DiffSynth-Studio/LTX-2-Repackage:text_encoder_post_modules.safetensors,DiffSynth-Studio/LTX-2-Repackage:video_vae_encoder.safetensors,DiffSynth-Studio/LTX-2-Repackage:audio_vae_encoder.safetensors,google/gemma-3-12b-it-qat-q4_0-unquantized:model-*.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LTX2-T2AV_lora-splited-cache" \ + --lora_base_model "dit" \ + --lora_target_modules "to_k,to_q,to_v,to_out.0" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --task "sft:data_process" + +accelerate launch examples/ltx2/model_training/train.py \ + --dataset_base_path ./models/train/LTX2-T2AV_lora-splited-cache \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio" \ + --height 512 \ + --width 768 \ + --num_frames 121 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "DiffSynth-Studio/LTX-2-Repackage:transformer.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LTX2-T2AV_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_k,to_q,to_v,to_out.0" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --task "sft:train" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/LTX-2.3-I2AV-splited.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/LTX-2.3-I2AV-splited.sh new file mode 100644 index 0000000000000000000000000000000000000000..35239b0889b6802414b8efe02b05da0c6a291cb6 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/LTX-2.3-I2AV-splited.sh @@ -0,0 +1,41 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "ltx2/LTX-2.3-I2AV-splited/*" --local_dir ./data/diffsynth_example_dataset + +# Splited Training +accelerate launch examples/ltx2/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/ltx2/LTX-2.3-I2AV-splited \ + --dataset_metadata_path data/diffsynth_example_dataset/ltx2/LTX-2.3-I2AV-splited/metadata.csv \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio,input_image" \ + --height 512 \ + --width 768 \ + --num_frames 121 \ + --dataset_repeat 1 \ + --model_id_with_origin_paths "DiffSynth-Studio/LTX-2.3-Repackage:text_encoder_post_modules.safetensors,DiffSynth-Studio/LTX-2.3-Repackage:video_vae_encoder.safetensors,DiffSynth-Studio/LTX-2.3-Repackage:audio_vae_encoder.safetensors,google/gemma-3-12b-it-qat-q4_0-unquantized:model-*.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LTX2.3-I2AV_lora-splited-cache" \ + --lora_base_model "dit" \ + --lora_target_modules "to_k,to_q,to_v,to_out.0" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --task "sft:data_process" + +accelerate launch examples/ltx2/model_training/train.py \ + --dataset_base_path ./models/train/LTX2.3-I2AV_lora-splited-cache \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio,input_image" \ + --height 512 \ + --width 768 \ + --num_frames 121 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "DiffSynth-Studio/LTX-2.3-Repackage:transformer.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LTX2.3-I2AV_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_k,to_q,to_v,to_out.0" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --task "sft:train" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/LTX-2.3-T2AV-IC-LoRA-splited.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/LTX-2.3-T2AV-IC-LoRA-splited.sh new file mode 100644 index 0000000000000000000000000000000000000000..7cfb82ac7f2d437b60b18984e0653b271bb5d743 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/LTX-2.3-T2AV-IC-LoRA-splited.sh @@ -0,0 +1,41 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "ltx2/LTX-2.3-T2AV-IC-LoRA-splited/*" --local_dir ./data/diffsynth_example_dataset + +# Splited Training +accelerate launch examples/ltx2/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/ltx2/LTX-2.3-T2AV-IC-LoRA-splited \ + --dataset_metadata_path data/diffsynth_example_dataset/ltx2/LTX-2.3-T2AV-IC-LoRA-splited/metadata.json \ + --data_file_keys "video,input_audio,in_context_videos" \ + --extra_inputs "input_audio,in_context_videos,in_context_downsample_factor,frame_rate" \ + --height 512 \ + --width 768 \ + --num_frames 81 \ + --dataset_repeat 1 \ + --model_id_with_origin_paths "DiffSynth-Studio/LTX-2.3-Repackage:text_encoder_post_modules.safetensors,DiffSynth-Studio/LTX-2.3-Repackage:video_vae_encoder.safetensors,DiffSynth-Studio/LTX-2.3-Repackage:audio_vae_encoder.safetensors,google/gemma-3-12b-it-qat-q4_0-unquantized:model-*.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LTX2.3-T2AV-IC-LoRA-splited-cache" \ + --lora_base_model "dit" \ + --lora_target_modules "to_k,to_q,to_v,to_out.0" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --task "sft:data_process" + +accelerate launch examples/ltx2/model_training/train.py \ + --dataset_base_path ./models/train/LTX2.3-T2AV-IC-LoRA-splited-cache \ + --data_file_keys "video,input_audio,in_context_videos" \ + --extra_inputs "input_audio,in_context_videos,in_context_downsample_factor,frame_rate" \ + --height 512 \ + --width 768 \ + --num_frames 81 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "DiffSynth-Studio/LTX-2.3-Repackage:transformer.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LTX2.3-T2AV-IC-LoRA" \ + --lora_base_model "dit" \ + --lora_target_modules "to_k,to_q,to_v,to_out.0" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --task "sft:train" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/LTX-2.3-T2AV-splited.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/LTX-2.3-T2AV-splited.sh new file mode 100644 index 0000000000000000000000000000000000000000..ff13a9ded983b2005ee16774137076454f92c98c --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/lora/LTX-2.3-T2AV-splited.sh @@ -0,0 +1,41 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "ltx2/LTX-2.3-T2AV-splited/*" --local_dir ./data/diffsynth_example_dataset + +# Splited Training +accelerate launch examples/ltx2/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/ltx2/LTX-2.3-T2AV-splited \ + --dataset_metadata_path data/diffsynth_example_dataset/ltx2/LTX-2.3-T2AV-splited/metadata.csv \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio" \ + --height 512 \ + --width 768 \ + --num_frames 121 \ + --dataset_repeat 1 \ + --model_id_with_origin_paths "DiffSynth-Studio/LTX-2.3-Repackage:text_encoder_post_modules.safetensors,DiffSynth-Studio/LTX-2.3-Repackage:video_vae_encoder.safetensors,DiffSynth-Studio/LTX-2.3-Repackage:audio_vae_encoder.safetensors,google/gemma-3-12b-it-qat-q4_0-unquantized:model-*.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LTX2.3-T2AV_lora-splited-cache" \ + --lora_base_model "dit" \ + --lora_target_modules "to_k,to_q,to_v,to_out.0" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --task "sft:data_process" + +accelerate launch examples/ltx2/model_training/train.py \ + --dataset_base_path ./models/train/LTX2.3-T2AV_lora-splited-cache \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio" \ + --height 512 \ + --width 768 \ + --num_frames 121 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "DiffSynth-Studio/LTX-2.3-Repackage:transformer.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LTX2.3-T2AV_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_k,to_q,to_v,to_out.0" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --task "sft:train" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/scripts/split_model_statedicts.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/scripts/split_model_statedicts.py new file mode 100644 index 0000000000000000000000000000000000000000..1ca2ff3258bdf3aeaf98dce59351ecbc07cd8fdb --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/scripts/split_model_statedicts.py @@ -0,0 +1,104 @@ +from safetensors.torch import save_file +from diffsynth import hash_state_dict_keys +from diffsynth.core import load_state_dict +from diffsynth.models.model_loader import ModelPool + +model_pool = ModelPool() +state_dict = load_state_dict("models/Lightricks/LTX-2/ltx-2-19b-dev.safetensors") + +dit_state_dict = {} +for name in state_dict: + if name.startswith("model.diffusion_model."): + new_name = name.replace("model.diffusion_model.", "") + if new_name.startswith("audio_embeddings_connector.") or new_name.startswith("video_embeddings_connector."): + continue + dit_state_dict[name] = state_dict[name] + +print(f"dit_state_dict keys hash: {hash_state_dict_keys(dit_state_dict)}") +save_file(dit_state_dict, "models/DiffSynth-Studio/LTX-2-Repackage/transformer.safetensors") +model_pool.auto_load_model( + "models/DiffSynth-Studio/LTX-2-Repackage/transformer.safetensors", +) + + +video_vae_encoder_state_dict = {} +for name in state_dict: + if name.startswith("vae.encoder."): + video_vae_encoder_state_dict[name] = state_dict[name] + elif name.startswith("vae.per_channel_statistics."): + video_vae_encoder_state_dict[name] = state_dict[name] + +save_file(video_vae_encoder_state_dict, "models/DiffSynth-Studio/LTX-2-Repackage/video_vae_encoder.safetensors") +print(f"video_vae_encoder keys hash: {hash_state_dict_keys(video_vae_encoder_state_dict)}") +model_pool.auto_load_model("models/DiffSynth-Studio/LTX-2-Repackage/video_vae_encoder.safetensors") + + +video_vae_decoder_state_dict = {} +for name in state_dict: + if name.startswith("vae.decoder."): + video_vae_decoder_state_dict[name] = state_dict[name] + elif name.startswith("vae.per_channel_statistics."): + video_vae_decoder_state_dict[name] = state_dict[name] +save_file(video_vae_decoder_state_dict, "models/DiffSynth-Studio/LTX-2-Repackage/video_vae_decoder.safetensors") +print(f"video_vae_decoder keys hash: {hash_state_dict_keys(video_vae_decoder_state_dict)}") +model_pool.auto_load_model("models/DiffSynth-Studio/LTX-2-Repackage/video_vae_decoder.safetensors") + + +audio_vae_decoder_state_dict = {} +for name in state_dict: + if name.startswith("audio_vae.decoder."): + audio_vae_decoder_state_dict[name] = state_dict[name] + elif name.startswith("audio_vae.per_channel_statistics."): + audio_vae_decoder_state_dict[name] = state_dict[name] +save_file(audio_vae_decoder_state_dict, "models/DiffSynth-Studio/LTX-2-Repackage/audio_vae_decoder.safetensors") +print(f"audio_vae_decoder keys hash: {hash_state_dict_keys(audio_vae_decoder_state_dict)}") +model_pool.auto_load_model("models/DiffSynth-Studio/LTX-2-Repackage/audio_vae_decoder.safetensors") + + +audio_vae_encoder_state_dict = {} +for name in state_dict: + if name.startswith("audio_vae.encoder."): + audio_vae_encoder_state_dict[name] = state_dict[name] + elif name.startswith("audio_vae.per_channel_statistics."): + audio_vae_encoder_state_dict[name] = state_dict[name] +save_file(audio_vae_encoder_state_dict, "models/DiffSynth-Studio/LTX-2-Repackage/audio_vae_encoder.safetensors") +print(f"audio_vae_encoder keys hash: {hash_state_dict_keys(audio_vae_encoder_state_dict)}") +model_pool.auto_load_model("models/DiffSynth-Studio/LTX-2-Repackage/audio_vae_encoder.safetensors") + + +audio_vocoder_state_dict = {} +for name in state_dict: + if name.startswith("vocoder."): + audio_vocoder_state_dict[name] = state_dict[name] +save_file(audio_vocoder_state_dict, "models/DiffSynth-Studio/LTX-2-Repackage/audio_vocoder.safetensors") +print(f"audio_vocoder keys hash: {hash_state_dict_keys(audio_vocoder_state_dict)}") +model_pool.auto_load_model("models/DiffSynth-Studio/LTX-2-Repackage/audio_vocoder.safetensors") + + +text_encoder_post_modules_state_dict = {} +for name in state_dict: + if name.startswith("text_embedding_projection."): + text_encoder_post_modules_state_dict[name] = state_dict[name] + elif name.startswith("model.diffusion_model.video_embeddings_connector."): + text_encoder_post_modules_state_dict[name] = state_dict[name] + elif name.startswith("model.diffusion_model.audio_embeddings_connector."): + text_encoder_post_modules_state_dict[name] = state_dict[name] +save_file(text_encoder_post_modules_state_dict, "models/DiffSynth-Studio/LTX-2-Repackage/text_encoder_post_modules.safetensors") +print(f"text_encoder_post_modules keys hash: {hash_state_dict_keys(text_encoder_post_modules_state_dict)}") +model_pool.auto_load_model("models/DiffSynth-Studio/LTX-2-Repackage/text_encoder_post_modules.safetensors") + + +state_dict = load_state_dict("models/Lightricks/LTX-2/ltx-2-19b-distilled.safetensors") +dit_state_dict = {} +for name in state_dict: + if name.startswith("model.diffusion_model."): + new_name = name.replace("model.diffusion_model.", "") + if new_name.startswith("audio_embeddings_connector.") or new_name.startswith("video_embeddings_connector."): + continue + dit_state_dict[name] = state_dict[name] + +print(f"dit_state_dict keys hash: {hash_state_dict_keys(dit_state_dict)}") +save_file(dit_state_dict, "models/DiffSynth-Studio/LTX-2-Repackage/transformer_distilled.safetensors") +model_pool.auto_load_model( + "models/DiffSynth-Studio/LTX-2-Repackage/transformer_distilled.safetensors", +) \ No newline at end of file diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/scripts/split_model_statedicts_ltx2.3.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/scripts/split_model_statedicts_ltx2.3.py new file mode 100644 index 0000000000000000000000000000000000000000..1dbc1bb3f6d5f1739a1d68b46f02eddd4403fb42 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/scripts/split_model_statedicts_ltx2.3.py @@ -0,0 +1,102 @@ +from safetensors.torch import save_file +from diffsynth import hash_state_dict_keys +from diffsynth.core import load_state_dict +from diffsynth.models.model_loader import ModelPool +import os + +model_pool = ModelPool() +state_dict = load_state_dict("models/Lightricks/LTX-2.3/ltx-2.3-22b-dev.safetensors") +os.makedirs("models/DiffSynth-Studio/LTX-2.3-Repackage", exist_ok=True) + +dit_state_dict = {} +for name in state_dict: + if name.startswith("model.diffusion_model."): + new_name = name.replace("model.diffusion_model.", "") + if new_name.startswith("audio_embeddings_connector.") or new_name.startswith("video_embeddings_connector."): + continue + dit_state_dict[name] = state_dict[name] + +print(f"dit_state_dict keys hash: {hash_state_dict_keys(dit_state_dict)}") +save_file(dit_state_dict, "models/DiffSynth-Studio/LTX-2.3-Repackage/transformer.safetensors") +model_pool.auto_load_model("models/DiffSynth-Studio/LTX-2.3-Repackage/transformer.safetensors") + + +video_vae_encoder_state_dict = {} +for name in state_dict: + if name.startswith("vae.encoder."): + video_vae_encoder_state_dict[name] = state_dict[name] + elif name.startswith("vae.per_channel_statistics."): + video_vae_encoder_state_dict[name] = state_dict[name] + +save_file(video_vae_encoder_state_dict, "models/DiffSynth-Studio/LTX-2.3-Repackage/video_vae_encoder.safetensors") +print(f"video_vae_encoder keys hash: {hash_state_dict_keys(video_vae_encoder_state_dict)}") +model_pool.auto_load_model("models/DiffSynth-Studio/LTX-2.3-Repackage/video_vae_encoder.safetensors") + + +video_vae_decoder_state_dict = {} +for name in state_dict: + if name.startswith("vae.decoder."): + video_vae_decoder_state_dict[name] = state_dict[name] + elif name.startswith("vae.per_channel_statistics."): + video_vae_decoder_state_dict[name] = state_dict[name] +save_file(video_vae_decoder_state_dict, "models/DiffSynth-Studio/LTX-2.3-Repackage/video_vae_decoder.safetensors") +print(f"video_vae_decoder keys hash: {hash_state_dict_keys(video_vae_decoder_state_dict)}") +model_pool.auto_load_model("models/DiffSynth-Studio/LTX-2.3-Repackage/video_vae_decoder.safetensors") + + +audio_vae_decoder_state_dict = {} +for name in state_dict: + if name.startswith("audio_vae.decoder."): + audio_vae_decoder_state_dict[name] = state_dict[name] + elif name.startswith("audio_vae.per_channel_statistics."): + audio_vae_decoder_state_dict[name] = state_dict[name] +save_file(audio_vae_decoder_state_dict, "models/DiffSynth-Studio/LTX-2.3-Repackage/audio_vae_decoder.safetensors") +print(f"audio_vae_decoder keys hash: {hash_state_dict_keys(audio_vae_decoder_state_dict)}") +model_pool.auto_load_model("models/DiffSynth-Studio/LTX-2.3-Repackage/audio_vae_decoder.safetensors") + + +audio_vae_encoder_state_dict = {} +for name in state_dict: + if name.startswith("audio_vae.encoder."): + audio_vae_encoder_state_dict[name] = state_dict[name] + elif name.startswith("audio_vae.per_channel_statistics."): + audio_vae_encoder_state_dict[name] = state_dict[name] +save_file(audio_vae_encoder_state_dict, "models/DiffSynth-Studio/LTX-2.3-Repackage/audio_vae_encoder.safetensors") +print(f"audio_vae_encoder keys hash: {hash_state_dict_keys(audio_vae_encoder_state_dict)}") +model_pool.auto_load_model("models/DiffSynth-Studio/LTX-2.3-Repackage/audio_vae_encoder.safetensors") + + +audio_vocoder_state_dict = {} +for name in state_dict: + if name.startswith("vocoder."): + audio_vocoder_state_dict[name] = state_dict[name] +save_file(audio_vocoder_state_dict, "models/DiffSynth-Studio/LTX-2.3-Repackage/audio_vocoder.safetensors") +print(f"audio_vocoder keys hash: {hash_state_dict_keys(audio_vocoder_state_dict)}") +model_pool.auto_load_model("models/DiffSynth-Studio/LTX-2.3-Repackage/audio_vocoder.safetensors") + + +text_encoder_post_modules_state_dict = {} +for name in state_dict: + if name.startswith("text_embedding_projection."): + text_encoder_post_modules_state_dict[name] = state_dict[name] + elif name.startswith("model.diffusion_model.video_embeddings_connector."): + text_encoder_post_modules_state_dict[name] = state_dict[name] + elif name.startswith("model.diffusion_model.audio_embeddings_connector."): + text_encoder_post_modules_state_dict[name] = state_dict[name] +save_file(text_encoder_post_modules_state_dict, "models/DiffSynth-Studio/LTX-2.3-Repackage/text_encoder_post_modules.safetensors") +print(f"text_encoder_post_modules keys hash: {hash_state_dict_keys(text_encoder_post_modules_state_dict)}") +model_pool.auto_load_model("models/DiffSynth-Studio/LTX-2.3-Repackage/text_encoder_post_modules.safetensors") + + +state_dict = load_state_dict("models/Lightricks/LTX-2.3/ltx-2.3-22b-distilled.safetensors") +dit_state_dict = {} +for name in state_dict: + if name.startswith("model.diffusion_model."): + new_name = name.replace("model.diffusion_model.", "") + if new_name.startswith("audio_embeddings_connector.") or new_name.startswith("video_embeddings_connector."): + continue + dit_state_dict[name] = state_dict[name] + +print(f"dit_state_dict keys hash: {hash_state_dict_keys(dit_state_dict)}") +save_file(dit_state_dict, "models/DiffSynth-Studio/LTX-2.3-Repackage/transformer_distilled.safetensors") +model_pool.auto_load_model("models/DiffSynth-Studio/LTX-2.3-Repackage/transformer_distilled.safetensors") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/train.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/train.py new file mode 100644 index 0000000000000000000000000000000000000000..f332fcd71f90eb7487f09d761bdb94f64dc61437 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/train.py @@ -0,0 +1,188 @@ +import torch, os, argparse, accelerate, warnings +from diffsynth.core import UnifiedDataset +from diffsynth.core.data.operators import LoadAudioWithTorchaudio, ToAbsolutePath, RouteByType, SequencialProcess +from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig +from diffsynth.diffusion import * +os.environ["TOKENIZERS_PARALLELISM"] = "false" + + +class LTX2TrainingModule(DiffusionTrainingModule): + def __init__( + self, + model_paths=None, model_id_with_origin_paths=None, + tokenizer_path=None, + trainable_models=None, + lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None, + preset_lora_path=None, preset_lora_model=None, + use_gradient_checkpointing=True, + use_gradient_checkpointing_offload=False, + extra_inputs=None, + fp8_models=None, + offload_models=None, + resume_from_checkpoint=None, remove_prefix_in_ckpt=None, + device="cpu", + task="sft", + ): + super().__init__() + # Warning + if not use_gradient_checkpointing: + warnings.warn("Gradient checkpointing is detected as disabled. To prevent out-of-memory errors, the training framework will forcibly enable gradient checkpointing.") + use_gradient_checkpointing = True + + # Load models + model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, fp8_models=fp8_models, offload_models=offload_models, device=device) + tokenizer_config = ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized") if tokenizer_path is None else ModelConfig(tokenizer_path) + self.pipe = LTX2AudioVideoPipeline.from_pretrained(torch_dtype=torch.bfloat16, device=device, model_configs=model_configs, tokenizer_config=tokenizer_config) + self.pipe = self.split_pipeline_units( + task, self.pipe, trainable_models, lora_base_model, + remove_unnecessary_params=True, + force_remove_params_shared=("audio_latents", "video_latents"), + force_remove_params_nega=("audio_context", "video_context") + ) + self.resume_from_checkpoint(resume_from_checkpoint, remove_prefix_in_ckpt) + # Training mode + self.switch_pipe_to_training_mode( + self.pipe, trainable_models, + lora_base_model, lora_target_modules, lora_rank, lora_checkpoint, + preset_lora_path, preset_lora_model, + task=task, + ) + + # Store other configs + self.use_gradient_checkpointing = use_gradient_checkpointing + self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload + self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else [] + self.fp8_models = fp8_models + self.task = task + self.task_to_loss = { + "sft:data_process": lambda pipe, *args: args, + "sft": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTAudioVideoLoss(pipe, **inputs_shared, **inputs_posi), + "sft:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTAudioVideoLoss(pipe, **inputs_shared, **inputs_posi), + } + + def parse_extra_inputs(self, data, extra_inputs, inputs_shared): + for extra_input in extra_inputs: + if extra_input == "input_image": + inputs_shared["input_images"] = [data["video"][0]] + inputs_shared["input_images_indexes"] = [0] + inputs_shared["input_images_strength"] = 1.0 + else: + inputs_shared[extra_input] = data[extra_input] + return inputs_shared + + def get_pipeline_inputs(self, data): + inputs_posi = {"prompt": data["prompt"]} + inputs_nega = {} + inputs_shared = { + # Assume you are using this pipeline for inference, + # please fill in the input parameters. + "input_video": data["video"], + "height": data["video"][0].size[1], + "width": data["video"][0].size[0], + "num_frames": len(data["video"]), + "frame_rate": data.get("frame_rate", 24), + # Please do not modify the following parameters + # unless you clearly know what this will cause. + "cfg_scale": 1, + "tiled": False, + "rand_device": self.pipe.device, + "use_gradient_checkpointing": self.use_gradient_checkpointing, + "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload, + "video_patchifier": self.pipe.video_patchifier, + "audio_patchifier": self.pipe.audio_patchifier, + } + inputs_shared = self.parse_extra_inputs(data, self.extra_inputs, inputs_shared) + return inputs_shared, inputs_posi, inputs_nega + + def forward(self, data, inputs=None): + if inputs is None: inputs = self.get_pipeline_inputs(data) + inputs = self.transfer_data_to_device(inputs, self.pipe.device, self.pipe.torch_dtype) + for unit in self.pipe.units: + inputs = self.pipe.unit_runner(unit, self.pipe, *inputs) + loss = self.task_to_loss[self.task](self.pipe, *inputs) + return loss + + +def ltx2_parser(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser = add_general_config(parser) + parser = add_video_size_config(parser) + parser.add_argument("--tokenizer_path", type=str, default=None, help="Path to tokenizer.") + parser.add_argument("--frame_rate", type=float, default=24, help="frame rate of the training videos.") + parser.add_argument("--initialize_model_on_cpu", default=False, action="store_true", help="Whether to initialize models on CPU.") + return parser + + +if __name__ == "__main__": + parser = ltx2_parser() + args = parser.parse_args() + accelerator = accelerate.Accelerator( + gradient_accumulation_steps=args.gradient_accumulation_steps, + kwargs_handlers=[accelerate.DistributedDataParallelKwargs(find_unused_parameters=args.find_unused_parameters)], + ) + video_processor = UnifiedDataset.default_video_operator( + base_path=args.dataset_base_path, + max_pixels=args.max_pixels, + height=args.height, + width=args.width, + height_division_factor=32, + width_division_factor=32, + num_frames=args.num_frames, + time_division_factor=8, + time_division_remainder=1, + frame_rate=args.frame_rate, + fix_frame_rate=True, + ) + dataset = UnifiedDataset( + base_path=args.dataset_base_path, + metadata_path=args.dataset_metadata_path, + repeat=args.dataset_repeat, + data_file_keys=args.data_file_keys.split(","), + main_data_operator=video_processor, + special_operator_map={ + "input_audio": ToAbsolutePath(args.dataset_base_path) >> LoadAudioWithTorchaudio(num_frames=args.num_frames, time_division_factor=8, time_division_remainder=1, frame_rate=args.frame_rate), + "in_context_videos": RouteByType(operator_map=[ + (str, video_processor), + (list, SequencialProcess(video_processor)), + ]), + } + ) + model = LTX2TrainingModule( + model_paths=args.model_paths, + model_id_with_origin_paths=args.model_id_with_origin_paths, + tokenizer_path=args.tokenizer_path, + trainable_models=args.trainable_models, + lora_base_model=args.lora_base_model, + lora_target_modules=args.lora_target_modules, + lora_rank=args.lora_rank, + lora_checkpoint=args.lora_checkpoint, + preset_lora_path=args.preset_lora_path, + preset_lora_model=args.preset_lora_model, + use_gradient_checkpointing=args.use_gradient_checkpointing, + use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload, + extra_inputs=args.extra_inputs, + fp8_models=args.fp8_models, + offload_models=args.offload_models, + resume_from_checkpoint=args.resume_from_checkpoint, + remove_prefix_in_ckpt=args.remove_prefix_in_ckpt, + task=args.task, + device="cpu" if (args.initialize_model_on_cpu or args.enable_model_cpu_offload) else accelerator.device, + ) + model_logger = ModelLogger( + args.output_path, + remove_prefix_in_ckpt=args.remove_prefix_in_ckpt, + enable_tensorboard_log=args.enable_tensorboard_log, + enable_swanlab_log=args.enable_swanlab_log, + swanlab_project=args.swanlab_project, + enable_wandb_log=args.enable_wandb_log, + wandb_project=args.wandb_project, + ) + launcher_map = { + "sft:data_process": launch_data_process_task, + "direct_distill:data_process": launch_data_process_task, + "sft": launch_training_task, + "sft:train": launch_training_task, + "direct_distill": launch_training_task, + "direct_distill:train": launch_training_task, + } + launcher_map[args.task](accelerator, dataset, model, model_logger, args=args) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_full/JoyAI-Echo-T2AV.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_full/JoyAI-Echo-T2AV.py new file mode 100644 index 0000000000000000000000000000000000000000..f9589324b5ac774fbc2746026d5300ba3bbd8d4a --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_full/JoyAI-Echo-T2AV.py @@ -0,0 +1,51 @@ +import torch +from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig +from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2 +from diffsynth import load_state_dict + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} + +pipe = LTX2AudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config), + ModelConfig(model_id="jd-opensource/JoyAI-Echo", origin_file_pattern="JoyAI-Echo-release.safetensors", **vram_config), + + ], + tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"), +) + +state_dict = load_state_dict("models/train/JoyAI-Echo-T2AV-full/epoch-4.safetensors") +pipe.dit.load_state_dict(state_dict) + +prompt = "A beautiful sunset over the ocean." + +negative_prompt = "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts." +height, width, num_frames = 512, 768, 121 +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + seed=43, + height=height, + width=width, + num_frames=num_frames, + use_distilled_pipeline=True, + tiled=True, +) +write_video_audio_ltx2( + video=video, + audio=audio, + output_path='joyai_echo.mp4', + fps=24, + audio_sample_rate=pipe.audio_vocoder.output_sampling_rate, +) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_full/LTX-2-T2AV.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_full/LTX-2-T2AV.py new file mode 100644 index 0000000000000000000000000000000000000000..6201ec138a111177e7fae61e1220cc39af49ab20 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_full/LTX-2-T2AV.py @@ -0,0 +1,47 @@ +import torch +from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig +from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2 + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = LTX2AudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="text_encoder_post_modules.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="video_vae_decoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vae_decoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vocoder.safetensors", **vram_config), + ModelConfig(path="./models/train/LTX2-T2AV-full/epoch-4.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"), +) +prompt = "A beautiful sunset over the ocean." +negative_prompt = "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts." +height, width, num_frames = 512, 768, 121 +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + seed=43, + height=height, + width=width, + num_frames=num_frames, + tiled=True, + cfg_scale=4.0 +) +write_video_audio_ltx2( + video=video, + audio=audio, + output_path='ltx2_onestage.mp4', + fps=24, + audio_sample_rate=24000, +) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_full/LTX-2.3-I2AV.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_full/LTX-2.3-I2AV.py new file mode 100644 index 0000000000000000000000000000000000000000..f375ee60736d30dc37ef00a9328f3885d831b8b1 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_full/LTX-2.3-I2AV.py @@ -0,0 +1,54 @@ +import torch +from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig +from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2 +from diffsynth.utils.data import VideoData + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = LTX2AudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="text_encoder_post_modules.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="video_vae_decoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="audio_vae_decoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="audio_vocoder.safetensors", **vram_config), + ModelConfig(path="./models/train/LTX2.3-I2AV-full/epoch-4.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"), +) + +prompt = "A beautiful sunset over the ocean." +negative_prompt = "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts." +height, width, num_frames = 512, 768, 121 +image = VideoData("data/example_video_dataset/ltx2/video.mp4", height=height, width=width)[0] +# first frame +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + seed=43, + height=height, + width=width, + num_frames=num_frames, + tiled=False, + input_images=[image], + input_images_indexes=[0], + input_images_strength=1.0, + num_inference_steps=40, +) +write_video_audio_ltx2( + video=video, + audio=audio, + output_path='ltx2.3_onestage_i2av_first.mp4', + fps=24, + audio_sample_rate=pipe.audio_vocoder.output_sampling_rate, +) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_full/LTX-2.3-T2AV.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_full/LTX-2.3-T2AV.py new file mode 100644 index 0000000000000000000000000000000000000000..d8392546ee5000368d992101e1e6c71fabcf7536 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_full/LTX-2.3-T2AV.py @@ -0,0 +1,47 @@ +import torch +from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig +from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2 + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = LTX2AudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="text_encoder_post_modules.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="video_vae_decoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="audio_vae_decoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="audio_vocoder.safetensors", **vram_config), + ModelConfig(path="./models/train/LTX2.3-T2AV-full/epoch-4.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"), +) +prompt = "A beautiful sunset over the ocean." +negative_prompt = "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts." +height, width, num_frames = 512, 768, 121 +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + seed=43, + height=height, + width=width, + num_frames=num_frames, + tiled=True, + cfg_scale=4.0 +) +write_video_audio_ltx2( + video=video, + audio=audio, + output_path='ltx2_onestage.mp4', + fps=24, + audio_sample_rate=24000, +) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/JoyAI-Echo-T2AV.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/JoyAI-Echo-T2AV.py new file mode 100644 index 0000000000000000000000000000000000000000..4f5296959efae63a25eae9900f93ba09ab8c9c06 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/JoyAI-Echo-T2AV.py @@ -0,0 +1,49 @@ +import torch +from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig +from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2 + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} + +pipe = LTX2AudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config), + ModelConfig(model_id="jd-opensource/JoyAI-Echo", origin_file_pattern="JoyAI-Echo-release.safetensors", **vram_config), + + ], + tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"), +) + +pipe.load_lora(pipe.dit, "models/train/JoyAI-Echo-T2AV_lora/epoch-4.safetensors") + +prompt = "A beautiful sunset over the ocean." + +negative_prompt = "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts." +height, width, num_frames = 512, 768, 121 +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + seed=43, + height=height, + width=width, + num_frames=num_frames, + use_distilled_pipeline=True, + tiled=True, +) +write_video_audio_ltx2( + video=video, + audio=audio, + output_path='joyai_echo.mp4', + fps=24, + audio_sample_rate=pipe.audio_vocoder.output_sampling_rate, +) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/LTX-2-T2AV-IC-LoRA.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/LTX-2-T2AV-IC-LoRA.py new file mode 100644 index 0000000000000000000000000000000000000000..d6eda1a9605760c391e8addb41e29976dcabd0c8 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/LTX-2-T2AV-IC-LoRA.py @@ -0,0 +1,56 @@ +import torch +from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig +from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2 +from diffsynth.utils.data import VideoData + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = LTX2AudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="transformer.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="text_encoder_post_modules.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="video_vae_decoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vae_decoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vocoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="video_vae_encoder.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"), +) +pipe.load_lora(pipe.dit, "./models/train/LTX2-T2AV-IC-LoRA/epoch-4.safetensors") +prompt = "[VISUAL]:Two cute orange cats, wearing boxing gloves, stand on a boxing ring and fight each other. [SOUNDS]:the sound of two cats boxing" +negative_prompt = "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts." +height, width, num_frames = 512, 768, 81 +ref_scale_factor = 2 +frame_rate = 24 +input_video = VideoData("data/example_video_dataset/ltx2/depth_video.mp4", height=height // ref_scale_factor // 2, width=width // ref_scale_factor // 2) +input_video = input_video.raw_data() +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + seed=43, + height=height, + width=width, + num_frames=num_frames, + frame_rate=frame_rate, + tiled=True, + in_context_videos=[input_video], + in_context_downsample_factor=ref_scale_factor, +) +write_video_audio_ltx2( + video=video, + audio=audio, + output_path='ltx2_onestage_ic.mp4', + fps=frame_rate, + audio_sample_rate=24000, +) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/LTX-2-T2AV.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/LTX-2-T2AV.py new file mode 100644 index 0000000000000000000000000000000000000000..d0dab817d289522f94af0a3dc957c50305908419 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/LTX-2-T2AV.py @@ -0,0 +1,48 @@ +import torch +from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig +from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2 + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = LTX2AudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="transformer.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="text_encoder_post_modules.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="video_vae_decoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vae_decoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vocoder.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"), +) +pipe.load_lora(pipe.dit, "models/train/LTX2-T2AV_lora/epoch-4.safetensors") +prompt = "A beautiful sunset over the ocean." +negative_prompt = "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts." +height, width, num_frames = 512, 768, 121 +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + seed=43, + height=height, + width=width, + num_frames=num_frames, + tiled=True, + cfg_scale=4.0 +) +write_video_audio_ltx2( + video=video, + audio=audio, + output_path='ltx2_onestage.mp4', + fps=24, + audio_sample_rate=24000, +) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/LTX-2-T2AV_noaudio.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/LTX-2-T2AV_noaudio.py new file mode 100644 index 0000000000000000000000000000000000000000..336b2bf5b978eacd4c8e41668dd38649936c7620 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/LTX-2-T2AV_noaudio.py @@ -0,0 +1,48 @@ +import torch +from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig +from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2 + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = LTX2AudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="transformer.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="text_encoder_post_modules.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="video_vae_decoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vae_decoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vocoder.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"), +) +pipe.load_lora(pipe.dit, "models/train/LTX2-T2AV-noaudio_lora/epoch-4.safetensors") +prompt = "A beautiful sunset over the ocean." +negative_prompt = "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts." +height, width, num_frames = 512, 768, 121 +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + seed=43, + height=height, + width=width, + num_frames=num_frames, + tiled=True, + cfg_scale=4.0 +) +write_video_audio_ltx2( + video=video, + audio=audio, + output_path='ltx2_onestage.mp4', + fps=24, + audio_sample_rate=24000, +) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/LTX-2.3-I2AV.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/LTX-2.3-I2AV.py new file mode 100644 index 0000000000000000000000000000000000000000..dc4093076b1dbdce4bb498c081f96cc73a43475f --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/LTX-2.3-I2AV.py @@ -0,0 +1,56 @@ +import torch +from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig +from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2 +from diffsynth.utils.data import VideoData + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = LTX2AudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="transformer.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="text_encoder_post_modules.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="video_vae_decoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="audio_vae_decoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="audio_vocoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="video_vae_encoder.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"), +) +pipe.load_lora(pipe.dit, "models/train/LTX2.3-I2AV_lora/epoch-4.safetensors") + +prompt = "A beautiful sunset over the ocean." +negative_prompt = "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts." +height, width, num_frames = 512, 768, 121 +image = VideoData("data/example_video_dataset/ltx2/video.mp4", height=height, width=width)[0] +# first frame +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + seed=43, + height=height, + width=width, + num_frames=num_frames, + tiled=False, + input_images=[image], + input_images_indexes=[0], + input_images_strength=1.0, + num_inference_steps=40, +) +write_video_audio_ltx2( + video=video, + audio=audio, + output_path='ltx2.3_onestage_i2av_first.mp4', + fps=24, + audio_sample_rate=pipe.audio_vocoder.output_sampling_rate, +) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/LTX-2.3-T2AV-IC-LoRA.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/LTX-2.3-T2AV-IC-LoRA.py new file mode 100644 index 0000000000000000000000000000000000000000..4ade9a72df5aace48da3a30ce04b71ecd3e9b8af --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/LTX-2.3-T2AV-IC-LoRA.py @@ -0,0 +1,56 @@ +import torch +from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig +from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2 +from diffsynth.utils.data import VideoData + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = LTX2AudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="transformer.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="text_encoder_post_modules.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="video_vae_decoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="audio_vae_decoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="audio_vocoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="video_vae_encoder.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"), +) +pipe.load_lora(pipe.dit, "./models/train/LTX2.3-T2AV-IC-LoRA/epoch-4.safetensors") +prompt = "[VISUAL]:Two cute orange cats, wearing boxing gloves, stand on a boxing ring and fight each other. [SOUNDS]:the sound of two cats boxing" +negative_prompt = "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts." +height, width, num_frames = 512, 768, 81 +ref_scale_factor = 2 +frame_rate = 24 +input_video = VideoData("data/example_video_dataset/ltx2/depth_video.mp4", height=height // ref_scale_factor // 2, width=width // ref_scale_factor // 2) +input_video = input_video.raw_data() +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + seed=43, + height=height, + width=width, + num_frames=num_frames, + frame_rate=frame_rate, + tiled=True, + in_context_videos=[input_video], + in_context_downsample_factor=ref_scale_factor, +) +write_video_audio_ltx2( + video=video, + audio=audio, + output_path='ltx2.3_onestage_ic.mp4', + fps=frame_rate, + audio_sample_rate=pipe.audio_vocoder.output_sampling_rate, +) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/LTX-2.3-T2AV.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/LTX-2.3-T2AV.py new file mode 100644 index 0000000000000000000000000000000000000000..03d974b1ec4ac486b9501da7926dcf5844619031 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/ltx2/model_training/validate_lora/LTX-2.3-T2AV.py @@ -0,0 +1,48 @@ +import torch +from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig +from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2 + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = LTX2AudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="transformer.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="text_encoder_post_modules.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="video_vae_decoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="audio_vae_decoder.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="audio_vocoder.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"), +) +pipe.load_lora(pipe.dit, "models/train/LTX2.3-T2AV_lora/epoch-4.safetensors") +prompt = "A beautiful sunset over the ocean." +negative_prompt = "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts." +height, width, num_frames = 512, 768, 121 +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + seed=43, + height=height, + width=width, + num_frames=num_frames, + tiled=True, + cfg_scale=4.0 +) +write_video_audio_ltx2( + video=video, + audio=audio, + output_path='ltx2_onestage.mp4', + fps=24, + audio_sample_rate=pipe.audio_vocoder.output_sampling_rate, +) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/README.md b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/README.md new file mode 100644 index 0000000000000000000000000000000000000000..71798325d5fd9b3aca5592854b097d66d8f5ee1a --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/README.md @@ -0,0 +1,3 @@ +English Document: https://diffsynth-studio-doc.readthedocs.io/en/latest/Model_Details/Wan.html + +中文文档：https://diffsynth-studio-doc.readthedocs.io/zh-cn/latest/Model_Details/Wan.html diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/acceleration/unified_sequence_parallel.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/acceleration/unified_sequence_parallel.py new file mode 100644 index 0000000000000000000000000000000000000000..d900368450ffe324ea771ac184413af4d126dfe0 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/acceleration/unified_sequence_parallel.py @@ -0,0 +1,55 @@ +import torch +from PIL import Image +from diffsynth.utils.data.audio_video import write_video_audio +from diffsynth.pipelines.mova_audio_video import MovaAudioVideoPipeline, ModelConfig +import torch.distributed as dist + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = MovaAudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + use_usp=True, + model_configs=[ + ModelConfig(model_id="openmoss/MOVA-360p", origin_file_pattern="video_dit/diffusion_pytorch_model-*.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-360p", origin_file_pattern="video_dit_2/diffusion_pytorch_model-*.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-360p", origin_file_pattern="audio_dit/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-360p", origin_file_pattern="dual_tower_bridge/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="audio_vae/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Wan-Series-Converted-Safetensors", origin_file_pattern="Wan2.1_VAE.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Wan-Series-Converted-Safetensors", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="tokenizer/"), +) +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" +) + +prompt = "Two cute orange cats, wearing boxing gloves, stand on a boxing ring and fight each other." +height, width, num_frames = 352, 640, 121 +frame_rate=24 +input_image = Image.open("data/examples/wan/cat_fightning.jpg").resize((width, height)).convert("RGB") +# Image-to-video +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + height=height, + width=width, + num_frames=num_frames, + input_image=input_image, + num_inference_steps=50, + seed=0, + tiled=True, + frame_rate=frame_rate, +) +if dist.get_rank() == 0: + write_video_audio(video, audio, "MOVA-360p-cat.mp4", fps=24, audio_sample_rate=pipe.audio_vae.sample_rate) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_inference/MOVA-360p-I2AV.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_inference/MOVA-360p-I2AV.py new file mode 100644 index 0000000000000000000000000000000000000000..28e03f8ccd74a6cbda187db3b9704d3f34e54508 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_inference/MOVA-360p-I2AV.py @@ -0,0 +1,52 @@ +import torch +from PIL import Image +from diffsynth.pipelines.mova_audio_video import ModelConfig, MovaAudioVideoPipeline +from diffsynth.utils.data.audio_video import write_video_audio + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = MovaAudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="openmoss/MOVA-360p", origin_file_pattern="video_dit/diffusion_pytorch_model-*.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-360p", origin_file_pattern="video_dit_2/diffusion_pytorch_model-*.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-360p", origin_file_pattern="audio_dit/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-360p", origin_file_pattern="dual_tower_bridge/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="audio_vae/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Wan-Series-Converted-Safetensors", origin_file_pattern="Wan2.1_VAE.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Wan-Series-Converted-Safetensors", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="tokenizer/"), +) +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" +) + +prompt = "Two cute orange cats, wearing boxing gloves, stand on a boxing ring and fight each other." +height, width, num_frames = 352, 640, 121 +frame_rate = 24 +input_image = Image.open("data/examples/wan/cat_fightning.jpg").resize((width, height)).convert("RGB") +# Image-to-video +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + height=height, + width=width, + num_frames=num_frames, + input_image=input_image, + num_inference_steps=50, + seed=0, + tiled=True, + frame_rate=frame_rate, +) +write_video_audio(video, audio, "MOVA-360p-cat.mp4", fps=24, audio_sample_rate=pipe.audio_vae.sample_rate) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_inference/MOVA-720p-I2AV.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_inference/MOVA-720p-I2AV.py new file mode 100644 index 0000000000000000000000000000000000000000..b82c4fc3c1a1c9881004c58d47a3c1bd207c48da --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_inference/MOVA-720p-I2AV.py @@ -0,0 +1,52 @@ +import torch +from PIL import Image +from diffsynth.utils.data.audio_video import write_video_audio +from diffsynth.pipelines.mova_audio_video import MovaAudioVideoPipeline, ModelConfig + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = MovaAudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="video_dit/diffusion_pytorch_model-*.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="video_dit_2/diffusion_pytorch_model-*.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="audio_dit/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="dual_tower_bridge/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="audio_vae/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Wan-Series-Converted-Safetensors", origin_file_pattern="Wan2.1_VAE.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Wan-Series-Converted-Safetensors", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="tokenizer/"), +) + +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" +) +prompt = "Two cute orange cats, wearing boxing gloves, stand on a boxing ring and fight each other." +height, width, num_frames = 720, 1280, 121 +frame_rate = 24 +input_image = Image.open("data/examples/wan/cat_fightning.jpg").resize((width, height)).convert("RGB") +# Image-to-video +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + height=height, + width=width, + num_frames=num_frames, + input_image=input_image, + num_inference_steps=50, + seed=0, + tiled=True, + frame_rate=frame_rate, +) +write_video_audio(video, audio, "MOVA-720p-cat.mp4", fps=24, audio_sample_rate=pipe.audio_vae.sample_rate) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_inference_low_vram/MOVA-360p-I2AV.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_inference_low_vram/MOVA-360p-I2AV.py new file mode 100644 index 0000000000000000000000000000000000000000..badd949789d9f560fa80f3a269a9293bbe40632b --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_inference_low_vram/MOVA-360p-I2AV.py @@ -0,0 +1,53 @@ +import torch +from PIL import Image +from diffsynth.pipelines.mova_audio_video import ModelConfig, MovaAudioVideoPipeline +from diffsynth.utils.data.audio_video import write_video_audio + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = MovaAudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="openmoss/MOVA-360p", origin_file_pattern="video_dit/diffusion_pytorch_model-*.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-360p", origin_file_pattern="video_dit_2/diffusion_pytorch_model-*.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-360p", origin_file_pattern="audio_dit/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-360p", origin_file_pattern="dual_tower_bridge/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="audio_vae/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Wan-Series-Converted-Safetensors", origin_file_pattern="Wan2.1_VAE.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Wan-Series-Converted-Safetensors", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="tokenizer/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) +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" +) + +prompt = "Two cute orange cats, wearing boxing gloves, stand on a boxing ring and fight each other." +height, width, num_frames = 352, 640, 121 +frame_rate = 24 +input_image = Image.open("data/examples/wan/cat_fightning.jpg").resize((width, height)).convert("RGB") +# Image-to-video +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + height=height, + width=width, + num_frames=num_frames, + input_image=input_image, + num_inference_steps=50, + seed=0, + tiled=True, + frame_rate=frame_rate, +) +write_video_audio(video, audio, "MOVA-360p-cat.mp4", fps=24, audio_sample_rate=pipe.audio_vae.sample_rate) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_inference_low_vram/MOVA-720p-I2AV.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_inference_low_vram/MOVA-720p-I2AV.py new file mode 100644 index 0000000000000000000000000000000000000000..3d6888349f9021f910c2c42897b0501bb433250b --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_inference_low_vram/MOVA-720p-I2AV.py @@ -0,0 +1,53 @@ +import torch +from PIL import Image +from diffsynth.utils.data.audio_video import write_video_audio +from diffsynth.pipelines.mova_audio_video import MovaAudioVideoPipeline, ModelConfig + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = MovaAudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="video_dit/diffusion_pytorch_model-*.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="video_dit_2/diffusion_pytorch_model-*.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="audio_dit/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="dual_tower_bridge/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="audio_vae/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Wan-Series-Converted-Safetensors", origin_file_pattern="Wan2.1_VAE.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Wan-Series-Converted-Safetensors", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="tokenizer/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +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" +) +prompt = "Two cute orange cats, wearing boxing gloves, stand on a boxing ring and fight each other." +height, width, num_frames = 720, 1280, 121 +frame_rate = 24 +input_image = Image.open("data/examples/wan/cat_fightning.jpg").resize((width, height)).convert("RGB") +# Image-to-video +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + height=height, + width=width, + num_frames=num_frames, + input_image=input_image, + num_inference_steps=50, + seed=0, + tiled=True, + frame_rate=frame_rate, +) +write_video_audio(video, audio, "MOVA-720p-cat.mp4", fps=24, audio_sample_rate=pipe.audio_vae.sample_rate) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/full/MOVA-360P-I2AV.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/full/MOVA-360P-I2AV.sh new file mode 100644 index 0000000000000000000000000000000000000000..d1026531cb6d64c81843d553a08aa8aaeb7c79dc --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/full/MOVA-360P-I2AV.sh @@ -0,0 +1,41 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "mova/MOVA-360P-I2AV/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/mova/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/mova/MOVA-360P-I2AV \ + --dataset_metadata_path data/diffsynth_example_dataset/mova/MOVA-360P-I2AV/metadata.csv \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio,input_image" \ + --height 352 \ + --width 640 \ + --num_frames 121 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "openmoss/MOVA-360p:video_dit/diffusion_pytorch_model-*.safetensors,openmoss/MOVA-360p:audio_dit/diffusion_pytorch_model.safetensors,openmoss/MOVA-360p:dual_tower_bridge/diffusion_pytorch_model.safetensors,openmoss/MOVA-720p:audio_vae/diffusion_pytorch_model.safetensors,DiffSynth-Studio/Wan-Series-Converted-Safetensors:Wan2.1_VAE.safetensors,DiffSynth-Studio/Wan-Series-Converted-Safetensors:models_t5_umt5-xxl-enc-bf16.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.video_dit." \ + --output_path "./models/train/MOVA-360p-I2AV_high_noise_full" \ + --trainable_models "dit" \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 \ + --use_gradient_checkpointing +# boundary corresponds to timesteps [900, 1000] + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/mova/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/mova/MOVA-360P-I2AV \ + --dataset_metadata_path data/diffsynth_example_dataset/mova/MOVA-360P-I2AV/metadata.csv \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio,input_image" \ + --height 352 \ + --width 640 \ + --num_frames 121 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "openmoss/MOVA-360p:video_dit_2/diffusion_pytorch_model-*.safetensors,openmoss/MOVA-360p:audio_dit/diffusion_pytorch_model.safetensors,openmoss/MOVA-360p:dual_tower_bridge/diffusion_pytorch_model.safetensors,openmoss/MOVA-720p:audio_vae/diffusion_pytorch_model.safetensors,DiffSynth-Studio/Wan-Series-Converted-Safetensors:Wan2.1_VAE.safetensors,DiffSynth-Studio/Wan-Series-Converted-Safetensors:models_t5_umt5-xxl-enc-bf16.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.video_dit." \ + --output_path "./models/train/MOVA-360p-I2AV_low_noise_full" \ + --trainable_models "dit" \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 \ + --use_gradient_checkpointing +# boundary corresponds to timesteps [0, 900) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/full/MOVA-720P-I2AV.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/full/MOVA-720P-I2AV.sh new file mode 100644 index 0000000000000000000000000000000000000000..378b2926dbdf3708a4590c499d1fd26acf019f7a --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/full/MOVA-720P-I2AV.sh @@ -0,0 +1,41 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "mova/MOVA-720P-I2AV/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/mova/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/mova/MOVA-720P-I2AV \ + --dataset_metadata_path data/diffsynth_example_dataset/mova/MOVA-720P-I2AV/metadata.csv \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio,input_image" \ + --height 720 \ + --width 1280 \ + --num_frames 121 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "openmoss/MOVA-720p:video_dit/diffusion_pytorch_model-*.safetensors,openmoss/MOVA-720p:audio_dit/diffusion_pytorch_model.safetensors,openmoss/MOVA-720p:dual_tower_bridge/diffusion_pytorch_model.safetensors,openmoss/MOVA-720p:audio_vae/diffusion_pytorch_model.safetensors,DiffSynth-Studio/Wan-Series-Converted-Safetensors:Wan2.1_VAE.safetensors,DiffSynth-Studio/Wan-Series-Converted-Safetensors:models_t5_umt5-xxl-enc-bf16.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.video_dit." \ + --output_path "./models/train/MOVA-720p-I2AV_high_noise_full" \ + --trainable_models "dit" \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 \ + --use_gradient_checkpointing +# boundary corresponds to timesteps [900, 1000] + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/mova/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/mova/MOVA-720P-I2AV \ + --dataset_metadata_path data/diffsynth_example_dataset/mova/MOVA-720P-I2AV/metadata.csv \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio,input_image" \ + --height 720 \ + --width 1280 \ + --num_frames 121 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "openmoss/MOVA-720p:video_dit_2/diffusion_pytorch_model-*.safetensors,openmoss/MOVA-720p:audio_dit/diffusion_pytorch_model.safetensors,openmoss/MOVA-720p:dual_tower_bridge/diffusion_pytorch_model.safetensors,openmoss/MOVA-720p:audio_vae/diffusion_pytorch_model.safetensors,DiffSynth-Studio/Wan-Series-Converted-Safetensors:Wan2.1_VAE.safetensors,DiffSynth-Studio/Wan-Series-Converted-Safetensors:models_t5_umt5-xxl-enc-bf16.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.video_dit." \ + --output_path "./models/train/MOVA-720p-I2AV_low_noise_full" \ + --trainable_models "dit" \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 \ + --use_gradient_checkpointing +# boundary corresponds to timesteps [0, 900) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/lora/MOVA-360P-I2AV.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/lora/MOVA-360P-I2AV.sh new file mode 100644 index 0000000000000000000000000000000000000000..10d46ba11d0a42f9df828e267bf750cad11bad50 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/lora/MOVA-360P-I2AV.sh @@ -0,0 +1,45 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "mova/MOVA-360P-I2AV/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/mova/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/mova/MOVA-360P-I2AV \ + --dataset_metadata_path data/diffsynth_example_dataset/mova/MOVA-360P-I2AV/metadata.csv \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio,input_image" \ + --height 352 \ + --width 640 \ + --num_frames 121 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "openmoss/MOVA-360p:video_dit/diffusion_pytorch_model-*.safetensors,openmoss/MOVA-360p:audio_dit/diffusion_pytorch_model.safetensors,openmoss/MOVA-360p:dual_tower_bridge/diffusion_pytorch_model.safetensors,openmoss/MOVA-720p:audio_vae/diffusion_pytorch_model.safetensors,DiffSynth-Studio/Wan-Series-Converted-Safetensors:Wan2.1_VAE.safetensors,DiffSynth-Studio/Wan-Series-Converted-Safetensors:models_t5_umt5-xxl-enc-bf16.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.video_dit." \ + --output_path "./models/train/MOVA-360p-I2AV_high_noise_lora" \ + --lora_base_model "video_dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 \ + --use_gradient_checkpointing +# boundary corresponds to timesteps [900, 1000] + +accelerate launch examples/mova/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/mova/MOVA-360P-I2AV \ + --dataset_metadata_path data/diffsynth_example_dataset/mova/MOVA-360P-I2AV/metadata.csv \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio,input_image" \ + --height 352 \ + --width 640 \ + --num_frames 121 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "openmoss/MOVA-360p:video_dit_2/diffusion_pytorch_model-*.safetensors,openmoss/MOVA-360p:audio_dit/diffusion_pytorch_model.safetensors,openmoss/MOVA-360p:dual_tower_bridge/diffusion_pytorch_model.safetensors,openmoss/MOVA-720p:audio_vae/diffusion_pytorch_model.safetensors,DiffSynth-Studio/Wan-Series-Converted-Safetensors:Wan2.1_VAE.safetensors,DiffSynth-Studio/Wan-Series-Converted-Safetensors:models_t5_umt5-xxl-enc-bf16.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.video_dit." \ + --output_path "./models/train/MOVA-360p-I2AV_low_noise_lora" \ + --lora_base_model "video_dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 \ + --use_gradient_checkpointing +# boundary corresponds to timesteps [0, 900) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/lora/MOVA-720P-I2AV.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/lora/MOVA-720P-I2AV.sh new file mode 100644 index 0000000000000000000000000000000000000000..02ca92ee2c2f07a26b67c0b4c9c90f955a51ad27 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/lora/MOVA-720P-I2AV.sh @@ -0,0 +1,45 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "mova/MOVA-720P-I2AV/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/mova/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/mova/MOVA-720P-I2AV \ + --dataset_metadata_path data/diffsynth_example_dataset/mova/MOVA-720P-I2AV/metadata.csv \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio,input_image" \ + --height 720 \ + --width 1280 \ + --num_frames 121 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "openmoss/MOVA-720p:video_dit/diffusion_pytorch_model-*.safetensors,openmoss/MOVA-720p:audio_dit/diffusion_pytorch_model.safetensors,openmoss/MOVA-720p:dual_tower_bridge/diffusion_pytorch_model.safetensors,openmoss/MOVA-720p:audio_vae/diffusion_pytorch_model.safetensors,DiffSynth-Studio/Wan-Series-Converted-Safetensors:Wan2.1_VAE.safetensors,DiffSynth-Studio/Wan-Series-Converted-Safetensors:models_t5_umt5-xxl-enc-bf16.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.video_dit." \ + --output_path "./models/train/MOVA-720p-I2AV_high_noise_lora" \ + --lora_base_model "video_dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 \ + --use_gradient_checkpointing +# boundary corresponds to timesteps [900, 1000] + +accelerate launch examples/mova/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/mova/MOVA-720P-I2AV \ + --dataset_metadata_path data/diffsynth_example_dataset/mova/MOVA-720P-I2AV/metadata.csv \ + --data_file_keys "video,input_audio" \ + --extra_inputs "input_audio,input_image" \ + --height 720 \ + --width 1280 \ + --num_frames 121 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "openmoss/MOVA-720p:video_dit_2/diffusion_pytorch_model-*.safetensors,openmoss/MOVA-720p:audio_dit/diffusion_pytorch_model.safetensors,openmoss/MOVA-720p:dual_tower_bridge/diffusion_pytorch_model.safetensors,openmoss/MOVA-720p:audio_vae/diffusion_pytorch_model.safetensors,DiffSynth-Studio/Wan-Series-Converted-Safetensors:Wan2.1_VAE.safetensors,DiffSynth-Studio/Wan-Series-Converted-Safetensors:models_t5_umt5-xxl-enc-bf16.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.video_dit." \ + --output_path "./models/train/MOVA-720p-I2AV_low_noise_lora" \ + --lora_base_model "video_dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 \ + --use_gradient_checkpointing +# boundary corresponds to timesteps [0, 900) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/train.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/train.py new file mode 100644 index 0000000000000000000000000000000000000000..ca32635c38c1a1ccb3796e9c4ff81c7a298b3cbb --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/train.py @@ -0,0 +1,202 @@ +import torch, os, argparse, accelerate, warnings +from diffsynth.core import UnifiedDataset +from diffsynth.core.data.operators import LoadAudioWithTorchaudio, ToAbsolutePath, RouteByType, SequencialProcess +from diffsynth.pipelines.mova_audio_video import MovaAudioVideoPipeline, ModelConfig +from diffsynth.diffusion import * +os.environ["TOKENIZERS_PARALLELISM"] = "false" + + +class MOVATrainingModule(DiffusionTrainingModule): + def __init__( + self, + model_paths=None, model_id_with_origin_paths=None, + tokenizer_path=None, + trainable_models=None, + lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None, + preset_lora_path=None, preset_lora_model=None, + use_gradient_checkpointing=True, + use_gradient_checkpointing_offload=False, + extra_inputs=None, + fp8_models=None, + offload_models=None, + resume_from_checkpoint=None, remove_prefix_in_ckpt=None, + device="cpu", + task="sft", + max_timestep_boundary=1.0, + min_timestep_boundary=0.0, + ): + super().__init__() + # Warning + if not use_gradient_checkpointing: + warnings.warn("Gradient checkpointing is detected as disabled. To prevent out-of-memory errors, the training framework will forcibly enable gradient checkpointing.") + use_gradient_checkpointing = True + + # Load models + model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, fp8_models=fp8_models, offload_models=offload_models, device=device) + tokenizer_config = ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized") if tokenizer_path is None else ModelConfig(tokenizer_path) + self.pipe = MovaAudioVideoPipeline.from_pretrained(torch_dtype=torch.bfloat16, device=device, model_configs=model_configs, tokenizer_config=tokenizer_config) + self.pipe = self.split_pipeline_units( + task, self.pipe, trainable_models, lora_base_model, + remove_unnecessary_params=True, + force_remove_params_shared=("audio_latents", "video_latents"), + force_remove_params_nega=("audio_context", "video_context") + ) + self.resume_from_checkpoint(resume_from_checkpoint, remove_prefix_in_ckpt) + # Training mode + self.switch_pipe_to_training_mode( + self.pipe, trainable_models, + lora_base_model, lora_target_modules, lora_rank, lora_checkpoint, + preset_lora_path, preset_lora_model, + task=task, + ) + + # Store other configs + self.use_gradient_checkpointing = use_gradient_checkpointing + self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload + self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else [] + self.fp8_models = fp8_models + self.task = task + self.task_to_loss = { + "sft:data_process": lambda pipe, *args: args, + "sft": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTAudioVideoLoss(pipe, **inputs_shared, **inputs_posi), + "sft:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTAudioVideoLoss(pipe, **inputs_shared, **inputs_posi), + } + self.max_timestep_boundary = max_timestep_boundary + self.min_timestep_boundary = min_timestep_boundary + + def parse_extra_inputs(self, data, extra_inputs, inputs_shared): + for extra_input in extra_inputs: + if extra_input == "input_image": + inputs_shared["input_image"] = data["video"][0] + else: + inputs_shared[extra_input] = data[extra_input] + return inputs_shared + + def get_pipeline_inputs(self, data): + inputs_posi = {"prompt": data["prompt"]} + inputs_nega = {} + inputs_shared = { + # Assume you are using this pipeline for inference, + # please fill in the input parameters. + "input_video": data["video"], + "height": data["video"][0].size[1], + "width": data["video"][0].size[0], + "num_frames": len(data["video"]), + "frame_rate": data.get("frame_rate", 24), + # Please do not modify the following parameters + # unless you clearly know what this will cause. + "cfg_scale": 1, + "tiled": False, + "rand_device": self.pipe.device, + "use_gradient_checkpointing": self.use_gradient_checkpointing, + "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload, + "max_timestep_boundary": self.max_timestep_boundary, + "min_timestep_boundary": self.min_timestep_boundary, + } + inputs_shared = self.parse_extra_inputs(data, self.extra_inputs, inputs_shared) + return inputs_shared, inputs_posi, inputs_nega + + def forward(self, data, inputs=None): + if inputs is None: inputs = self.get_pipeline_inputs(data) + inputs = self.transfer_data_to_device(inputs, self.pipe.device, self.pipe.torch_dtype) + for unit in self.pipe.units: + inputs = self.pipe.unit_runner(unit, self.pipe, *inputs) + loss = self.task_to_loss[self.task](self.pipe, *inputs) + return loss + + +def ltx2_parser(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser = add_general_config(parser) + parser = add_video_size_config(parser) + parser.add_argument("--tokenizer_path", type=str, default=None, help="Path to tokenizer.") + parser.add_argument("--frame_rate", type=float, default=24, help="Frame rate of the training videos. Mova is trained with a frame rate of 24, so it's recommended to use the same frame rate.") + parser.add_argument("--max_timestep_boundary", type=float, default=1.0, help="Max timestep boundary (for mixed models, e.g., Wan-AI/Wan2.2-I2V-A14B).") + parser.add_argument("--min_timestep_boundary", type=float, default=0.0, help="Min timestep boundary (for mixed models, e.g., Wan-AI/Wan2.2-I2V-A14B).") + parser.add_argument("--initialize_model_on_cpu", default=False, action="store_true", help="Whether to initialize models on CPU.") + return parser + + +if __name__ == "__main__": + parser = ltx2_parser() + args = parser.parse_args() + accelerator = accelerate.Accelerator( + gradient_accumulation_steps=args.gradient_accumulation_steps, + kwargs_handlers=[accelerate.DistributedDataParallelKwargs(find_unused_parameters=args.find_unused_parameters)], + ) + model = MOVATrainingModule( + model_paths=args.model_paths, + model_id_with_origin_paths=args.model_id_with_origin_paths, + tokenizer_path=args.tokenizer_path, + trainable_models=args.trainable_models, + lora_base_model=args.lora_base_model, + lora_target_modules=args.lora_target_modules, + lora_rank=args.lora_rank, + lora_checkpoint=args.lora_checkpoint, + preset_lora_path=args.preset_lora_path, + preset_lora_model=args.preset_lora_model, + use_gradient_checkpointing=args.use_gradient_checkpointing, + use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload, + extra_inputs=args.extra_inputs, + fp8_models=args.fp8_models, + offload_models=args.offload_models, + resume_from_checkpoint=args.resume_from_checkpoint, + remove_prefix_in_ckpt=args.remove_prefix_in_ckpt, + task=args.task, + device="cpu" if (args.initialize_model_on_cpu or args.enable_model_cpu_offload) else accelerator.device, + max_timestep_boundary=args.max_timestep_boundary, + min_timestep_boundary=args.min_timestep_boundary, + ) + video_processor = UnifiedDataset.default_video_operator( + base_path=args.dataset_base_path, + max_pixels=args.max_pixels, + height=args.height, + width=args.width, + height_division_factor=16, + width_division_factor=16, + num_frames=args.num_frames, + time_division_factor=4, + time_division_remainder=1, + frame_rate=args.frame_rate, + fix_frame_rate=True, + ) + dataset = UnifiedDataset( + base_path=args.dataset_base_path, + metadata_path=args.dataset_metadata_path, + repeat=args.dataset_repeat, + data_file_keys=args.data_file_keys.split(","), + main_data_operator=video_processor, + special_operator_map={ + "input_audio": + ToAbsolutePath(args.dataset_base_path) >> LoadAudioWithTorchaudio( + num_frames=args.num_frames, + time_division_factor=4, + time_division_remainder=1, + frame_rate=args.frame_rate, + ), + "in_context_videos": + RouteByType(operator_map=[ + (str, video_processor), + (list, SequencialProcess(video_processor)), + ]), + }, + ) + + model_logger = ModelLogger( + args.output_path, + remove_prefix_in_ckpt=args.remove_prefix_in_ckpt, + enable_tensorboard_log=args.enable_tensorboard_log, + enable_swanlab_log=args.enable_swanlab_log, + swanlab_project=args.swanlab_project, + enable_wandb_log=args.enable_wandb_log, + wandb_project=args.wandb_project, + ) + launcher_map = { + "sft:data_process": launch_data_process_task, + "direct_distill:data_process": launch_data_process_task, + "sft": launch_training_task, + "sft:train": launch_training_task, + "direct_distill": launch_training_task, + "direct_distill:train": launch_training_task, + } + launcher_map[args.task](accelerator, dataset, model, model_logger, args=args) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/validate_full/MOVA-360p-I2AV.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/validate_full/MOVA-360p-I2AV.py new file mode 100644 index 0000000000000000000000000000000000000000..606880a44802eeeb4ce733330dc209aeac37d0e3 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/validate_full/MOVA-360p-I2AV.py @@ -0,0 +1,53 @@ +import torch +from PIL import Image +from diffsynth.pipelines.mova_audio_video import ModelConfig, MovaAudioVideoPipeline +from diffsynth.utils.data.audio_video import write_video_audio +from diffsynth.utils.data import VideoData + + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = MovaAudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(path="./models/train/MOVA-360p-I2AV_high_noise_full/epoch-4.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-360p", origin_file_pattern="video_dit_2/diffusion_pytorch_model-*.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-360p", origin_file_pattern="audio_dit/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-360p", origin_file_pattern="dual_tower_bridge/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="audio_vae/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Wan-Series-Converted-Safetensors", origin_file_pattern="Wan2.1_VAE.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Wan-Series-Converted-Safetensors", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="tokenizer/"), +) +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" +) +prompt = "A beautiful sunset over the ocean." +height, width, num_frames = 352, 640, 121 +frame_rate = 24 +input_image = VideoData("data/example_video_dataset/ltx2/video.mp4", height=height, width=width)[0] +# Image-to-video +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + height=height, + width=width, + num_frames=num_frames, + input_image=input_image, + num_inference_steps=50, + seed=0, + tiled=True, + frame_rate=frame_rate, +) +write_video_audio(video, audio, "MOVA-360p.mp4", fps=24, audio_sample_rate=pipe.audio_vae.sample_rate) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/validate_full/MOVA-720p-I2AV.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/validate_full/MOVA-720p-I2AV.py new file mode 100644 index 0000000000000000000000000000000000000000..8c0ef824d17bb5d0bf8257fd0bc47979fca13348 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/validate_full/MOVA-720p-I2AV.py @@ -0,0 +1,54 @@ +import torch +from PIL import Image +from diffsynth.utils.data.audio_video import write_video_audio +from diffsynth.pipelines.mova_audio_video import MovaAudioVideoPipeline, ModelConfig +from diffsynth.utils.data import VideoData + + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = MovaAudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(path="./models/train/MOVA-720p-I2AV_high_noise_full/epoch-4.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="video_dit_2/diffusion_pytorch_model-*.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="audio_dit/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="dual_tower_bridge/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="audio_vae/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Wan-Series-Converted-Safetensors", origin_file_pattern="Wan2.1_VAE.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Wan-Series-Converted-Safetensors", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.video_dit, "models/train/MOVA-720p-I2AV_high_noise_lora/epoch-4.safetensors") +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" +) +prompt = "A beautiful sunset over the ocean." +height, width, num_frames = 720, 1280, 121 +frame_rate = 24 +input_image = VideoData("data/example_video_dataset/ltx2/video.mp4", height=height, width=width)[0] +# Image-to-video +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + height=height, + width=width, + num_frames=num_frames, + input_image=input_image, + num_inference_steps=50, + seed=0, + tiled=True, + frame_rate=frame_rate, +) +write_video_audio(video, audio, "MOVA-720p.mp4", fps=24, audio_sample_rate=pipe.audio_vae.sample_rate) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/validate_lora/MOVA-360p-I2AV.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/validate_lora/MOVA-360p-I2AV.py new file mode 100644 index 0000000000000000000000000000000000000000..00f43769099deb24575a05d0740bd6d3f9b07a33 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/validate_lora/MOVA-360p-I2AV.py @@ -0,0 +1,54 @@ +import torch +from PIL import Image +from diffsynth.pipelines.mova_audio_video import ModelConfig, MovaAudioVideoPipeline +from diffsynth.utils.data.audio_video import write_video_audio +from diffsynth.utils.data import VideoData + + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = MovaAudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="openmoss/MOVA-360p", origin_file_pattern="video_dit/diffusion_pytorch_model-*.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-360p", origin_file_pattern="video_dit_2/diffusion_pytorch_model-*.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-360p", origin_file_pattern="audio_dit/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-360p", origin_file_pattern="dual_tower_bridge/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="audio_vae/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Wan-Series-Converted-Safetensors", origin_file_pattern="Wan2.1_VAE.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Wan-Series-Converted-Safetensors", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.video_dit, "models/train/MOVA-360p-I2AV_high_noise_lora/epoch-4.safetensors") +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" +) +prompt = "A beautiful sunset over the ocean." +height, width, num_frames = 352, 640, 121 +frame_rate = 24 +input_image = VideoData("data/example_video_dataset/ltx2/video.mp4", height=height, width=width)[0] +# Image-to-video +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + height=height, + width=width, + num_frames=num_frames, + input_image=input_image, + num_inference_steps=50, + seed=0, + tiled=True, + frame_rate=frame_rate, +) +write_video_audio(video, audio, "MOVA-360p.mp4", fps=24, audio_sample_rate=pipe.audio_vae.sample_rate) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/validate_lora/MOVA-720p-I2AV.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/validate_lora/MOVA-720p-I2AV.py new file mode 100644 index 0000000000000000000000000000000000000000..282a8b09074877288b8a1cd695a6233412415248 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/mova/model_training/validate_lora/MOVA-720p-I2AV.py @@ -0,0 +1,54 @@ +import torch +from PIL import Image +from diffsynth.utils.data.audio_video import write_video_audio +from diffsynth.pipelines.mova_audio_video import MovaAudioVideoPipeline, ModelConfig +from diffsynth.utils.data import VideoData + + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = MovaAudioVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="video_dit/diffusion_pytorch_model-*.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="video_dit_2/diffusion_pytorch_model-*.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="audio_dit/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="dual_tower_bridge/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="audio_vae/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Wan-Series-Converted-Safetensors", origin_file_pattern="Wan2.1_VAE.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Wan-Series-Converted-Safetensors", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.video_dit, "models/train/MOVA-720p-I2AV_high_noise_lora/epoch-4.safetensors") +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" +) +prompt = "A beautiful sunset over the ocean." +height, width, num_frames = 720, 1280, 121 +frame_rate = 24 +input_image = VideoData("data/example_video_dataset/ltx2/video.mp4", height=height, width=width)[0] +# Image-to-video +video, audio = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + height=height, + width=width, + num_frames=num_frames, + input_image=input_image, + num_inference_steps=50, + seed=0, + tiled=True, + frame_rate=frame_rate, +) +write_video_audio(video, audio, "MOVA-720p.mp4", fps=24, audio_sample_rate=pipe.audio_vae.sample_rate) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/README.md b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/README.md new file mode 100644 index 0000000000000000000000000000000000000000..aee9807383b1d069c8351c1db5c38e3d42839c48 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/README.md @@ -0,0 +1,3 @@ +English Document: https://diffsynth-studio-doc.readthedocs.io/en/latest/Model_Details/Qwen-Image.html + +中文文档：https://diffsynth-studio-doc.readthedocs.io/zh-cn/latest/Model_Details/Qwen-Image.html diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/FireRed-Image-Edit-1.0.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/FireRed-Image-Edit-1.0.py new file mode 100644 index 0000000000000000000000000000000000000000..10008e05a71def91afc5d0a604c3c17f1b40d396 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/FireRed-Image-Edit-1.0.py @@ -0,0 +1,43 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from modelscope import dataset_snapshot_download +from PIL import Image +import torch + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="FireRedTeam/FireRed-Image-Edit-1.0", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) + +dataset_snapshot_download( + "DiffSynth-Studio/example_image_dataset", + allow_file_pattern="qwen_image_edit/*", + local_dir="data/example_image_dataset", +) + +prompt = "生成这两个人的合影" +edit_image = [ + Image.open("data/example_image_dataset/qwen_image_edit/image1.jpg"), + Image.open("data/example_image_dataset/qwen_image_edit/image2.jpg"), +] +image = pipe( + prompt, + edit_image=edit_image, + seed=1, + num_inference_steps=40, + height=1152, + width=896, + edit_image_auto_resize=True, +) +image.save("image.jpg") + +# FireRedTeam/FireRed-Image-Edit-1.0 is a multi-image editing model. +# Please use a list to input `edit_image`, even if the input contains only one image. +# edit_image = [Image.open("image.jpg")] +# Please do not input the image directly. +# edit_image = Image.open("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/FireRed-Image-Edit-1.1.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/FireRed-Image-Edit-1.1.py new file mode 100644 index 0000000000000000000000000000000000000000..65a2467943f34ee3bfe33098a8908ffe7c213575 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/FireRed-Image-Edit-1.1.py @@ -0,0 +1,43 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from modelscope import dataset_snapshot_download +from PIL import Image +import torch + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="FireRedTeam/FireRed-Image-Edit-1.1", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) + +dataset_snapshot_download( + "DiffSynth-Studio/example_image_dataset", + allow_file_pattern="qwen_image_edit/*", + local_dir="data/example_image_dataset", +) + +prompt = "生成这两个人的合影" +edit_image = [ + Image.open("data/example_image_dataset/qwen_image_edit/image1.jpg"), + Image.open("data/example_image_dataset/qwen_image_edit/image2.jpg"), +] +image = pipe( + prompt, + edit_image=edit_image, + seed=1, + num_inference_steps=40, + height=1152, + width=896, + edit_image_auto_resize=True, +) +image.save("image.jpg") + +# FireRedTeam/FireRed-Image-Edit-1.1 is a multi-image editing model. +# Please use a list to input `edit_image`, even if the input contains only one image. +# edit_image = [Image.open("image.jpg")] +# Please do not input the image directly. +# edit_image = Image.open("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-2512.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-2512.py new file mode 100644 index 0000000000000000000000000000000000000000..d00cf96503e4d42d488979754fcaa2fd67f36c0f --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-2512.py @@ -0,0 +1,17 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-2512", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt, seed=0, num_inference_steps=40) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py new file mode 100644 index 0000000000000000000000000000000000000000..85b9b96886a3a48bdf30319a99557a952721ab81 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py @@ -0,0 +1,31 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput +from PIL import Image +import torch +from modelscope import dataset_snapshot_download + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny", origin_file_pattern="model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="canny/image_1.jpg" +) +controlnet_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328)) + +prompt = "一只小狗，毛发光洁柔顺，眼神灵动，背景是樱花纷飞的春日庭院，唯美温馨。" +image = pipe( + prompt, seed=0, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)] +) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py new file mode 100644 index 0000000000000000000000000000000000000000..6676868ad0e24d09d962211dda758f29cf7180f2 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py @@ -0,0 +1,32 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput +from PIL import Image +import torch +from modelscope import dataset_snapshot_download + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth", origin_file_pattern="model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="depth/image_1.jpg" +) + +controlnet_image = Image.open("data/example_image_dataset/depth/image_1.jpg").resize((1328, 1328)) + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe( + prompt, seed=0, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)] +) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py new file mode 100644 index 0000000000000000000000000000000000000000..1cb98e0a348ba5b9e9748496c05bc8fc8aef154e --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py @@ -0,0 +1,33 @@ +import torch +from PIL import Image +from modelscope import dataset_snapshot_download +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint", origin_file_pattern="model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="inpaint/*.jpg" +) +prompt = "a cat with sunglasses" +controlnet_image = Image.open("./data/example_image_dataset/inpaint/image_1.jpg").convert("RGB").resize((1328, 1328)) +inpaint_mask = Image.open("./data/example_image_dataset/inpaint/mask.jpg").convert("RGB").resize((1328, 1328)) +image = pipe( + prompt, seed=0, + input_image=controlnet_image, inpaint_mask=inpaint_mask, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image, inpaint_mask=inpaint_mask)], + num_inference_steps=40, +) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-InpaintCanny.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-InpaintCanny.py new file mode 100644 index 0000000000000000000000000000000000000000..f8af9e88377fe649dd1756ea98ae91e8331657f4 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-InpaintCanny.py @@ -0,0 +1,49 @@ +import torch +from PIL import Image +from modelscope import dataset_snapshot_download +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint", origin_file_pattern="model.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny", origin_file_pattern="model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="canny/*.jpg" +) +prompt = "一只小狗，毛发光洁柔顺，眼神灵动，背景是樱花纷飞的春日庭院，唯美温馨。" + +controlnet_canny_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328)) + +controlnet_inpaint_image = Image.open("./data/example_image_dataset/canny/image_2.jpg").convert("RGB").resize((1328, 1328)) +# generate a centered square mask +inpaint_mask = Image.new("L", controlnet_inpaint_image.size, 0) +mask_size = 512 +left = (controlnet_inpaint_image.width - mask_size) // 2 +top = (controlnet_inpaint_image.height - mask_size) // 2 +right = left + mask_size +bottom = top + mask_size +inpaint_mask.paste(255, (left, top, right, bottom)) +inpaint_mask = inpaint_mask.resize((1328, 1328)).convert("RGB") + +image = pipe( + prompt, seed=0, + input_image=controlnet_inpaint_image, inpaint_mask=inpaint_mask, + blockwise_controlnet_inputs=[ + ControlNetInput(image=controlnet_inpaint_image, inpaint_mask=inpaint_mask, controlnet_id=0), + ControlNetInput(image=controlnet_canny_image, controlnet_id=1), + ], + num_inference_steps=40, +) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Distill-DMD2.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Distill-DMD2.py new file mode 100644 index 0000000000000000000000000000000000000000..007538f76b5c63c3b72ab7d2cd592773677aabb5 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Distill-DMD2.py @@ -0,0 +1,25 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth.core import load_state_dict +from modelscope import snapshot_download +import torch, math + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) + +snapshot_download("MusePublic/Qwen-Image-Distill", allow_file_pattern="qwen_image_distill_3step.safetensors", cache_dir="models") +lora_state_dict = load_state_dict("models/MusePublic/Qwen-Image-Distill/qwen_image_distill_3step.safetensors") +lora_state_dict = {i.replace("base_model.model.", ""): j for i, j in lora_state_dict.items()} +pipe.load_lora(pipe.dit, state_dict=lora_state_dict) + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt, seed=0, num_inference_steps=3, cfg_scale=1, exponential_shift_mu=math.log(2.5)) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py new file mode 100644 index 0000000000000000000000000000000000000000..c13a417f5aba55820b8111b872e8cf7be26e6ed1 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py @@ -0,0 +1,17 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Distill-Full", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt, seed=0, num_inference_steps=15, cfg_scale=1) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py new file mode 100644 index 0000000000000000000000000000000000000000..aad1fdd7eb222e2d87fb57180c2ffb5d1db4c3ec --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py @@ -0,0 +1,20 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from modelscope import snapshot_download +import torch + +snapshot_download("DiffSynth-Studio/Qwen-Image-Distill-LoRA", local_dir="models/DiffSynth-Studio/Qwen-Image-Distill-LoRA") +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-Distill-LoRA/model.safetensors") + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt, seed=0, num_inference_steps=15, cfg_scale=1) +image.save("image.jpg") \ No newline at end of file diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py new file mode 100644 index 0000000000000000000000000000000000000000..5dcffbbf3112f84e7dcb6ecc2e73f4fb3ecb4af4 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py @@ -0,0 +1,31 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from PIL import Image +import torch + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit-2509", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) + +image_1 = pipe(prompt="一位少女", seed=0, num_inference_steps=40, height=1328, width=1024) +image_1.save("image1.jpg") + +image_2 = pipe(prompt="一位老人", seed=0, num_inference_steps=40, height=1328, width=1024) +image_2.save("image2.jpg") + +prompt = "生成这两个人的合影" +edit_image = [Image.open("image1.jpg"), Image.open("image2.jpg")] +image_3 = pipe(prompt, edit_image=edit_image, seed=1, num_inference_steps=40, height=1328, width=1024, edit_image_auto_resize=True) +image_3.save("image3.jpg") + +# Qwen-Image-Edit-2509 is a multi-image editing model. +# Please use a list to input `edit_image`, even if the input contains only one image. +# edit_image = [Image.open("image.jpg")] +# Please do not input the image directly. +# edit_image = Image.open("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Edit-2511-ICEdit.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Edit-2511-ICEdit.py new file mode 100644 index 0000000000000000000000000000000000000000..67eca5ac00ef7d483c4fc4c27a46e6d70748a94f --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Edit-2511-ICEdit.py @@ -0,0 +1,47 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from modelscope import snapshot_download +from PIL import Image +import torch + +# Load models +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit-2511", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +lora = ModelConfig( + model_id="DiffSynth-Studio/Qwen-Image-Edit-2511-ICEdit-LoRA", + origin_file_pattern="model.safetensors" +) +pipe.load_lora(pipe.dit, lora) + +# Load images +snapshot_download( + "DiffSynth-Studio/Qwen-Image-Edit-2511-ICEdit-LoRA", + local_dir="./data", + allow_file_pattern="assets/*" +) +edit_image = [ + Image.open("data/assets/image1_original.png"), + Image.open("data/assets/image1_edit_1.png"), + Image.open("data/assets/image2_original.png") +] +prompt = "Edit image 3 based on the transformation from image 1 to image 2." +negative_prompt = "泛黄，AI感，不真实，丑陋，油腻的皮肤，异常的肢体，不协调的肢体" + +# Generate +image_4 = pipe( + prompt=prompt, negative_prompt=negative_prompt, + edit_image=edit_image, + seed=1, + num_inference_steps=50, + height=1280, + width=720, + zero_cond_t=True, +) +image_4.save("image.png") \ No newline at end of file diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Edit-2511-Lightning.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Edit-2511-Lightning.py new file mode 100644 index 0000000000000000000000000000000000000000..c30ccba6959ec4ae9bd76c75a8dd329f83b87b5d --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Edit-2511-Lightning.py @@ -0,0 +1,53 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, FlowMatchScheduler +from modelscope import dataset_snapshot_download +from PIL import Image +import torch + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit-2511", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) + +lora = ModelConfig( + model_id="lightx2v/Qwen-Image-Edit-2511-Lightning", + origin_file_pattern="Qwen-Image-Edit-2511-Lightning-4steps-V1.0-bf16.safetensors" +) +pipe.load_lora(pipe.dit, lora, alpha=1) +pipe.scheduler = FlowMatchScheduler("Qwen-Image-Lightning") + + +dataset_snapshot_download( + "DiffSynth-Studio/example_image_dataset", + allow_file_pattern="qwen_image_edit/*", + local_dir="data/example_image_dataset", +) + +prompt = "生成这两个人的合影" +edit_image = [ + Image.open("data/example_image_dataset/qwen_image_edit/image1.jpg"), + Image.open("data/example_image_dataset/qwen_image_edit/image2.jpg"), +] +image = pipe( + prompt, + edit_image=edit_image, + seed=1, + num_inference_steps=4, + height=1152, + width=896, + edit_image_auto_resize=True, + zero_cond_t=True, # This is a special parameter introduced by Qwen-Image-Edit-2511 + cfg_scale=1.0, +) +image.save("image.jpg") + +# Qwen-Image-Edit-2511 is a multi-image editing model. +# Please use a list to input `edit_image`, even if the input contains only one image. +# edit_image = [Image.open("image.jpg")] +# Please do not input the image directly. +# edit_image = Image.open("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Edit-2511.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Edit-2511.py new file mode 100644 index 0000000000000000000000000000000000000000..c4d3c658bce2be9a6f845625a69101a931b88478 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Edit-2511.py @@ -0,0 +1,44 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from modelscope import dataset_snapshot_download +from PIL import Image +import torch + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit-2511", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) + +dataset_snapshot_download( + "DiffSynth-Studio/example_image_dataset", + allow_file_pattern="qwen_image_edit/*", + local_dir="data/example_image_dataset", +) + +prompt = "生成这两个人的合影" +edit_image = [ + Image.open("data/example_image_dataset/qwen_image_edit/image1.jpg"), + Image.open("data/example_image_dataset/qwen_image_edit/image2.jpg"), +] +image = pipe( + prompt, + edit_image=edit_image, + seed=1, + num_inference_steps=40, + height=1152, + width=896, + edit_image_auto_resize=True, + zero_cond_t=True, # This is a special parameter introduced by Qwen-Image-Edit-2511 +) +image.save("image.jpg") + +# Qwen-Image-Edit-2511 is a multi-image editing model. +# Please use a list to input `edit_image`, even if the input contains only one image. +# edit_image = [Image.open("image.jpg")] +# Please do not input the image directly. +# edit_image = Image.open("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py new file mode 100644 index 0000000000000000000000000000000000000000..c18eaa8fd329fda7f4784fe4f3ce22309bbda2ef --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py @@ -0,0 +1,25 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch +from modelscope import snapshot_download + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +snapshot_download("DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix", local_dir="models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix", allow_file_pattern="model.safetensors") +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix/model.safetensors") + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt=prompt, seed=0, num_inference_steps=40, height=1024, width=768) +image.save("image.jpg") + +prompt = "将裙子变成粉色" +image = image.resize((512, 384)) +image = pipe(prompt, edit_image=image, seed=1, num_inference_steps=40, height=1024, width=768, edit_rope_interpolation=True, edit_image_auto_resize=False) +image.save(f"image2.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Edit.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Edit.py new file mode 100644 index 0000000000000000000000000000000000000000..8a47756ffb71e778d204303bbb813c0fa97d987e --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Edit.py @@ -0,0 +1,25 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +input_image = pipe(prompt=prompt, seed=0, num_inference_steps=40, height=1328, width=1024) +input_image.save("image1.jpg") + +prompt = "将裙子改为粉色" +# edit_image_auto_resize=True: auto resize input image to match the area of 1024*1024 with the original aspect ratio +image = pipe(prompt, edit_image=input_image, seed=1, num_inference_steps=40, height=1328, width=1024, edit_image_auto_resize=True) +image.save(f"image2.jpg") + +# edit_image_auto_resize=False: do not resize input image +image = pipe(prompt, edit_image=input_image, seed=1, num_inference_steps=40, height=1328, width=1024, edit_image_auto_resize=False) +image.save(f"image3.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf0a12afd80cbde3d711f0942d593afc38591a0 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py @@ -0,0 +1,114 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch +from PIL import Image, ImageDraw, ImageFont +from modelscope import dataset_snapshot_download, snapshot_download +import random + + +def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False): + # Create a blank image for overlays + overlay = Image.new('RGBA', image.size, (0, 0, 0, 0)) + + colors = [ + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + ] + # Generate random colors for each mask + if use_random_colors: + colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))] + + # Font settings + try: + font = ImageFont.truetype("wqy-zenhei.ttc", font_size) # Adjust as needed + except IOError: + font = ImageFont.load_default(font_size) + + # Overlay each mask onto the overlay image + for mask, mask_prompt, color in zip(masks, mask_prompts, colors): + # Convert mask to RGBA mode + mask_rgba = mask.convert('RGBA') + mask_data = mask_rgba.getdata() + new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data] + mask_rgba.putdata(new_data) + + # Draw the mask prompt text on the mask + draw = ImageDraw.Draw(mask_rgba) + mask_bbox = mask.getbbox() # Get the bounding box of the mask + text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position + draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font) + + # Alpha composite the overlay with this mask + overlay = Image.alpha_composite(overlay, mask_rgba) + + # Composite the overlay onto the original image + result = Image.alpha_composite(image.convert('RGBA'), overlay) + + # Save or display the resulting image + result.save(output_path) + + return result + + +def example(pipe, seeds, example_id, global_prompt, entity_prompts, height=784, width=1280): + dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/eligen/poster/example_{example_id}/*.png" + ) + masks = [ + Image.open(f"./data/examples/eligen/poster/example_{example_id}/{i}.png").convert('RGB').resize((width, height)) + for i in range(len(entity_prompts)) + ] + negative_prompt = "网格化，规则的网格，模糊, 低分辨率, 低质量, 变形, 畸形, 错误的解剖学, 变形的手, 变形的身体, 变形的脸, 变形的头发, 变形的眼睛, 变形的嘴巴" + for seed in seeds: + # generate image + image = pipe( + prompt=global_prompt, + cfg_scale=4.0, + negative_prompt=negative_prompt, + num_inference_steps=40, + seed=seed, + height=height, + width=width, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks, + ) + image.save(f"eligen_poster_example_{example_id}_{seed}.png") + image = Image.new("RGB", (width, height), (0, 0, 0)) + visualize_masks(image, masks, entity_prompts, f"eligen_poster_example_{example_id}_mask_{seed}.png") + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +snapshot_download( + "DiffSynth-Studio/Qwen-Image-EliGen-Poster", + local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen-Poster", + allow_file_pattern="model.safetensors", +) +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen-Poster/model.safetensors") +global_prompt = "一张以柔粉紫为背景的海报，左侧有大号粉紫色文字“Qwen-Image EliGen-Poster”，粉紫色椭圆框内白色小字：“图像精确分区控制模型”。右侧有一只小兔子在拆礼物，旁边站着一只头顶迷你烟花发射器的小龙（卡通Q版）。背景有一些白云点缀。整体风格卡通可爱，传达节日惊喜的主题。" +entity_prompts = ["粉紫色文字“Qwen-Image EliGen-Poster”", "粉紫色椭圆框内白色小字：“图像精确分区控制模型”", "一只小兔子在拆礼物，小兔子旁边站着一只头顶迷你烟花发射器的小龙（卡通Q版）"] +seed = [42] +example(pipe, seed, 1, global_prompt, entity_prompts) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py new file mode 100644 index 0000000000000000000000000000000000000000..82bab2d0236c058216f23406be1220db3096cdfc --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py @@ -0,0 +1,106 @@ +import torch +import random +from PIL import Image, ImageDraw, ImageFont +from modelscope import dataset_snapshot_download, snapshot_download +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig + +def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False): + # Create a blank image for overlays + overlay = Image.new('RGBA', image.size, (0, 0, 0, 0)) + + colors = [ + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + ] + # Generate random colors for each mask + if use_random_colors: + colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))] + + # Font settings + try: + font = ImageFont.truetype("wqy-zenhei.ttc", font_size) # Adjust as needed + except IOError: + font = ImageFont.load_default(font_size) + + # Overlay each mask onto the overlay image + for mask, mask_prompt, color in zip(masks, mask_prompts, colors): + # Convert mask to RGBA mode + mask_rgba = mask.convert('RGBA') + mask_data = mask_rgba.getdata() + new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data] + mask_rgba.putdata(new_data) + + # Draw the mask prompt text on the mask + draw = ImageDraw.Draw(mask_rgba) + mask_bbox = mask.getbbox() # Get the bounding box of the mask + text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position + draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font) + + # Alpha composite the overlay with this mask + overlay = Image.alpha_composite(overlay, mask_rgba) + + # Composite the overlay onto the original image + result = Image.alpha_composite(image.convert('RGBA'), overlay) + + # Save or display the resulting image + result.save(output_path) + + return result + +def example(pipe, seeds, example_id, global_prompt, entity_prompts): + dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/qwen-image/example_{example_id}/*.png") + masks = [Image.open(f"./data/examples/eligen/qwen-image/example_{example_id}/{i}.png").convert('RGB').resize((1024, 1024)) for i in range(len(entity_prompts))] + negative_prompt = "网格化，规则的网格，模糊, 低分辨率, 低质量, 变形, 畸形, 错误的解剖学, 变形的手, 变形的身体, 变形的脸, 变形的头发, 变形的眼睛, 变形的嘴巴" + for seed in seeds: + # generate image + image = pipe( + prompt=global_prompt, + cfg_scale=4.0, + negative_prompt=negative_prompt, + num_inference_steps=40, + seed=seed, + height=1024, + width=1024, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks, + ) + image.save(f"eligen_example_{example_id}_{seed}.png") + visualize_masks(image, masks, entity_prompts, f"eligen_example_{example_id}_mask_{seed}.png") + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +snapshot_download("DiffSynth-Studio/Qwen-Image-EliGen-V2", local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen-V2", allow_file_pattern="model.safetensors") +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen-V2/model.safetensors") + +seeds = [0] + +global_prompt = "写实摄影风格. A beautiful asia woman wearing white dress, she is holding a mirror with her right arm, with a beach background." +entity_prompts = ["A beautiful woman", "mirror", "necklace", "glasses", "earring", "white dress", "jewelry headpiece"] +example(pipe, seeds, 7, global_prompt, entity_prompts) + +global_prompt = "写实摄影风格, 细节丰富。街头一位漂亮的女孩，穿着衬衫和短裤，手持写有“实体控制”的标牌，背景是繁忙的城市街道，阳光明媚，行人匆匆。" +entity_prompts = ["一个漂亮的女孩", "标牌 '实体控制'", "短裤", "衬衫"] +example(pipe, seeds, 4, global_prompt, entity_prompts) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-EliGen.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-EliGen.py new file mode 100644 index 0000000000000000000000000000000000000000..d49112317f3278d56df045cec7f26155773e869f --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-EliGen.py @@ -0,0 +1,107 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch +from PIL import Image, ImageDraw, ImageFont +from modelscope import dataset_snapshot_download, snapshot_download +import random + + +def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False): + # Create a blank image for overlays + overlay = Image.new('RGBA', image.size, (0, 0, 0, 0)) + + colors = [ + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + ] + # Generate random colors for each mask + if use_random_colors: + colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))] + + # Font settings + try: + font = ImageFont.truetype("wqy-zenhei.ttc", font_size) # Adjust as needed + except IOError: + font = ImageFont.load_default(font_size) + + # Overlay each mask onto the overlay image + for mask, mask_prompt, color in zip(masks, mask_prompts, colors): + # Convert mask to RGBA mode + mask_rgba = mask.convert('RGBA') + mask_data = mask_rgba.getdata() + new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data] + mask_rgba.putdata(new_data) + + # Draw the mask prompt text on the mask + draw = ImageDraw.Draw(mask_rgba) + mask_bbox = mask.getbbox() # Get the bounding box of the mask + text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position + draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font) + + # Alpha composite the overlay with this mask + overlay = Image.alpha_composite(overlay, mask_rgba) + + # Composite the overlay onto the original image + result = Image.alpha_composite(image.convert('RGBA'), overlay) + + # Save or display the resulting image + result.save(output_path) + + return result + +def example(pipe, seeds, example_id, global_prompt, entity_prompts): + dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/qwen-image/example_{example_id}/*.png") + masks = [Image.open(f"./data/examples/eligen/qwen-image/example_{example_id}/{i}.png").convert('RGB') for i in range(len(entity_prompts))] + negative_prompt = "" + for seed in seeds: + # generate image + image = pipe( + prompt=global_prompt, + cfg_scale=4.0, + negative_prompt=negative_prompt, + num_inference_steps=30, + seed=seed, + height=1024, + width=1024, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks, + ) + image.save(f"eligen_example_{example_id}_{seed}.png") + visualize_masks(image, masks, entity_prompts, f"eligen_example_{example_id}_mask_{seed}.png") + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +snapshot_download("DiffSynth-Studio/Qwen-Image-EliGen", local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen", allow_file_pattern="model.safetensors") +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen/model.safetensors") + +# example 1 +global_prompt = "A breathtaking beauty of Raja Ampat by the late-night moonlight , one beautiful woman from behind wearing a pale blue long dress with soft glow, sitting at the top of a cliff looking towards the beach,pastell light colors, a group of small distant birds flying in far sky, a boat sailing on the sea, best quality, realistic, whimsical, fantastic, splash art, intricate detailed, hyperdetailed, maximalist style, photorealistic, concept art, sharp focus, harmony, serenity, tranquility, soft pastell colors,ambient occlusion, cozy ambient lighting, masterpiece, liiv1, linquivera, metix, mentixis, masterpiece, award winning, view from above\n" +entity_prompts = ["cliff", "sea", "moon", "sailing boat", "a seated beautiful woman", "pale blue long dress with soft glow"] +example(pipe, [0], 1, global_prompt, entity_prompts) + +# example 2 +global_prompt = "samurai girl wearing a kimono, she's holding a sword glowing with red flame, her long hair is flowing in the wind, she is looking at a small bird perched on the back of her hand. ultra realist style. maximum image detail. maximum realistic render." +entity_prompts = ["flowing hair", "sword glowing with red flame", "A cute bird", "yellow belt"] +example(pipe, [0], 2, global_prompt, entity_prompts) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py new file mode 100644 index 0000000000000000000000000000000000000000..516ee08258c66ce86f40a13034f47d81ccfa62e1 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py @@ -0,0 +1,35 @@ +from PIL import Image +import torch +from modelscope import dataset_snapshot_download, snapshot_download +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth.utils.controlnet import Annotator + +allow_file_pattern = ["sk_model.pth", "sk_model2.pth", "dpt_hybrid-midas-501f0c75.pt", "ControlNetHED.pth", "body_pose_model.pth", "hand_pose_model.pth", "facenet.pth", "scannet.pt"] +snapshot_download("lllyasviel/Annotators", local_dir="models/Annotators", allow_file_pattern=allow_file_pattern) + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +snapshot_download("DiffSynth-Studio/Qwen-Image-In-Context-Control-Union", local_dir="models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union", allow_file_pattern="model.safetensors") +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union/model.safetensors") + +dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/qwen-image-context-control/image.jpg") +origin_image = Image.open("data/examples/qwen-image-context-control/image.jpg").resize((1024, 1024)) +annotator_ids = ['openpose', 'canny', 'depth', 'lineart', 'softedge', 'normal'] +for annotator_id in annotator_ids: + annotator = Annotator(processor_id=annotator_id, device="cuda") + control_image = annotator(origin_image) + control_image.save(f"{annotator.processor_id}.png") + + control_prompt = "Context_Control. " + prompt = f"{control_prompt}一个穿着淡蓝色的漂亮女孩正在翩翩起舞，背景是梦幻的星空，光影交错，细节精致。" + negative_prompt = "网格化，规则的网格，模糊, 低分辨率, 低质量, 变形, 畸形, 错误的解剖学, 变形的手, 变形的身体, 变形的脸, 变形的头发, 变形的眼睛, 变形的嘴巴" + image = pipe(prompt, seed=1, negative_prompt=negative_prompt, context_image=control_image, height=1024, width=1024) + image.save(f"image_{annotator.processor_id}.png") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Layered-Control-V2.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Layered-Control-V2.py new file mode 100644 index 0000000000000000000000000000000000000000..ce9968c7c4cd57230ca6cb1eb76a62725f0b1291 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Layered-Control-V2.py @@ -0,0 +1,43 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from modelscope import dataset_snapshot_download +from PIL import Image +import torch + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Layered-Control", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Layered-Control-V2", origin_file_pattern="model.safetensors")) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="layer_v2/*.png" +) + +prompt = "Text 'APRIL'" +input_image = Image.open("data/example_image_dataset/layer_v2/image_1.png").convert("RGBA").resize((1024, 1024)) +image = pipe( + prompt, seed=0, + height=1024, width=1024, + layer_input_image=input_image, layer_num=0, + num_inference_steps=10, cfg_scale=4, +) +image[0].save("image_prompt.png") + +mask_image = Image.open("data/example_image_dataset/layer_v2/mask_2.png").convert("RGBA").resize((1024, 1024)) +input_image = Image.open("data/example_image_dataset/layer_v2/image_2.png").convert("RGBA").resize((1024, 1024)) +image = pipe( + prompt, seed=0, + height=1024, width=1024, + layer_input_image=input_image, layer_num=0, + context_image=mask_image, + num_inference_steps=10, cfg_scale=1.0, +) +image[0].save("image_mask.png") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Layered-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Layered-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..5ce82c7ebc9b7ccf1d356b697a7684ea25e11c60 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Layered-Control.py @@ -0,0 +1,34 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from modelscope import snapshot_download +from PIL import Image +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Layered-Control", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) + +snapshot_download( + model_id="DiffSynth-Studio/Qwen-Image-Layered-Control", + allow_file_pattern="assets/image_1_input.png", + local_dir="data/layered_input" +) + +prompt = "A cartoon skeleton character wearing a purple hat and holding a gift box" +input_image = Image.open("data/layered_input/assets/image_1_input.png").convert("RGBA").resize((1024, 1024)) +images = pipe( + prompt, + seed=0, + num_inference_steps=30, cfg_scale=4, + height=1024, width=1024, + layer_input_image=input_image, + layer_num=0, +) +images[0].save("image.png") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Layered.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Layered.py new file mode 100644 index 0000000000000000000000000000000000000000..95aa4752c040eed3570ae0ef75a00581aec35218 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-Layered.py @@ -0,0 +1,36 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from modelscope import dataset_snapshot_download +from PIL import Image +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) + +dataset_snapshot_download( + "DiffSynth-Studio/example_image_dataset", + allow_patterns="layer/image.png", + local_dir="data/example_image_dataset" +) + +# Prompt should be provided to the pipeline. Our pipeline will not generate the prompt. +prompt = 'A cheerful child with brown hair is waving enthusiastically under a bright blue sky filled with colorful confetti and balloons. The word "HELLO!" is prominently displayed in bold red letters above the child, while "Have a Great Day!" appears in elegant cursive at the bottom right corner. The scene is vibrant and festive, with a mix of pastel colors and dynamic shapes creating a joyful atmosphere.' +# Height and width should be consistent with input_image and be divided evenly by 16 +input_image = Image.open("data/example_image_dataset/layer/image.png").convert("RGBA").resize((864, 480)) +images = pipe( + prompt, + seed=1, num_inference_steps=50, + height=480, width=864, + layer_input_image=input_image, layer_num=3, +) +for i, image in enumerate(images): + if i == 0: continue # The first image is the input image. + image.save(f"image_{i}.png") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-i2L.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-i2L.py new file mode 100644 index 0000000000000000000000000000000000000000..87061d829ff35742d4ad21d9e870c48f842c8077 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image-i2L.py @@ -0,0 +1,110 @@ +from diffsynth.pipelines.qwen_image import ( + QwenImagePipeline, ModelConfig, + QwenImageUnit_Image2LoRAEncode, QwenImageUnit_Image2LoRADecode +) +from diffsynth.utils.lora import merge_lora +from diffsynth import load_state_dict +from modelscope import snapshot_download +from safetensors.torch import save_file +import torch +from PIL import Image + + +def demo_style(): + # Load models + pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="SigLIP2-G384/model.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="DINOv3-7B/model.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-i2L", origin_file_pattern="Qwen-Image-i2L-Style.safetensors"), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), + ) + + # Load images + snapshot_download( + model_id="DiffSynth-Studio/Qwen-Image-i2L", + allow_file_pattern="assets/style/1/*", + local_dir="data/examples" + ) + images = [ + Image.open("data/examples/assets/style/1/0.jpg"), + Image.open("data/examples/assets/style/1/1.jpg"), + Image.open("data/examples/assets/style/1/2.jpg"), + Image.open("data/examples/assets/style/1/3.jpg"), + Image.open("data/examples/assets/style/1/4.jpg"), + ] + + # Model inference + with torch.no_grad(): + embs = QwenImageUnit_Image2LoRAEncode().process(pipe, image2lora_images=images) + lora = QwenImageUnit_Image2LoRADecode().process(pipe, **embs)["lora"] + save_file(lora, "model_style.safetensors") + + +def demo_coarse_fine_bias(): + # Load models + pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="SigLIP2-G384/model.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="DINOv3-7B/model.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-i2L", origin_file_pattern="Qwen-Image-i2L-Coarse.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-i2L", origin_file_pattern="Qwen-Image-i2L-Fine.safetensors"), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), + ) + + # Load images + snapshot_download( + model_id="DiffSynth-Studio/Qwen-Image-i2L", + allow_file_pattern="assets/lora/3/*", + local_dir="data/examples" + ) + images = [ + Image.open("data/examples/assets/lora/3/0.jpg"), + Image.open("data/examples/assets/lora/3/1.jpg"), + Image.open("data/examples/assets/lora/3/2.jpg"), + Image.open("data/examples/assets/lora/3/3.jpg"), + Image.open("data/examples/assets/lora/3/4.jpg"), + Image.open("data/examples/assets/lora/3/5.jpg"), + ] + + # Model inference + with torch.no_grad(): + embs = QwenImageUnit_Image2LoRAEncode().process(pipe, image2lora_images=images) + lora = QwenImageUnit_Image2LoRADecode().process(pipe, **embs)["lora"] + lora_bias = ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-i2L", origin_file_pattern="Qwen-Image-i2L-Bias.safetensors") + lora_bias.download_if_necessary() + lora_bias = load_state_dict(lora_bias.path, torch_dtype=torch.bfloat16, device="cuda") + lora = merge_lora([lora, lora_bias]) + save_file(lora, "model_coarse_fine_bias.safetensors") + + +def generate_image(lora_path, prompt, seed): + pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), + ) + pipe.load_lora(pipe.dit, lora_path) + image = pipe(prompt, seed=seed, height=1024, width=1024, num_inference_steps=50) + return image + + +demo_style() +image = generate_image("model_style.safetensors", "a cat", 0) +image.save("image_1.jpg") + +demo_coarse_fine_bias() +image = generate_image("model_coarse_fine_bias.safetensors", "bowl", 1) +image.save("image_2.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image.py new file mode 100644 index 0000000000000000000000000000000000000000..275cfba8c33db6971b87493f2966b6b235744588 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference/Qwen-Image.py @@ -0,0 +1,17 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt, seed=0, num_inference_steps=40) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/FireRed-Image-Edit-1.0.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/FireRed-Image-Edit-1.0.py new file mode 100644 index 0000000000000000000000000000000000000000..81121663c850d8424d3f7bde22af36441a732b3b --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/FireRed-Image-Edit-1.0.py @@ -0,0 +1,54 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from modelscope import dataset_snapshot_download +from PIL import Image +import torch + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="FireRedTeam/FireRed-Image-Edit-1.0", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) + +dataset_snapshot_download( + "DiffSynth-Studio/example_image_dataset", + allow_file_pattern="qwen_image_edit/*", + local_dir="data/example_image_dataset", +) + +prompt = "生成这两个人的合影" +edit_image = [ + Image.open("data/example_image_dataset/qwen_image_edit/image1.jpg"), + Image.open("data/example_image_dataset/qwen_image_edit/image2.jpg"), +] +image = pipe( + prompt, + edit_image=edit_image, + seed=1, + num_inference_steps=40, + height=1152, + width=896, + edit_image_auto_resize=True, +) +image.save("image.jpg") + +# FireRedTeam/FireRed-Image-Edit-1.0 is a multi-image editing model. +# Please use a list to input `edit_image`, even if the input contains only one image. +# edit_image = [Image.open("image.jpg")] +# Please do not input the image directly. +# edit_image = Image.open("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/FireRed-Image-Edit-1.1.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/FireRed-Image-Edit-1.1.py new file mode 100644 index 0000000000000000000000000000000000000000..83dfd5ccdd66e556a2ecf5811fe53ed838a00ca7 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/FireRed-Image-Edit-1.1.py @@ -0,0 +1,54 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from modelscope import dataset_snapshot_download +from PIL import Image +import torch + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="FireRedTeam/FireRed-Image-Edit-1.1", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) + +dataset_snapshot_download( + "DiffSynth-Studio/example_image_dataset", + allow_file_pattern="qwen_image_edit/*", + local_dir="data/example_image_dataset", +) + +prompt = "生成这两个人的合影" +edit_image = [ + Image.open("data/example_image_dataset/qwen_image_edit/image1.jpg"), + Image.open("data/example_image_dataset/qwen_image_edit/image2.jpg"), +] +image = pipe( + prompt, + edit_image=edit_image, + seed=1, + num_inference_steps=40, + height=1152, + width=896, + edit_image_auto_resize=True, +) +image.save("image.jpg") + +# FireRedTeam/FireRed-Image-Edit-1.1 is a multi-image editing model. +# Please use a list to input `edit_image`, even if the input contains only one image. +# edit_image = [Image.open("image.jpg")] +# Please do not input the image directly. +# edit_image = Image.open("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-2512.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-2512.py new file mode 100644 index 0000000000000000000000000000000000000000..63229cd7c7c10020f4cb74f9cd3098fdc35ce8d0 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-2512.py @@ -0,0 +1,28 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-2512", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt, seed=0, num_inference_steps=40) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py new file mode 100644 index 0000000000000000000000000000000000000000..0f2deb2f1cd05e13073f7942b195b268a36d9a74 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py @@ -0,0 +1,42 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput +from PIL import Image +import torch +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny", origin_file_pattern="model.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="canny/image_1.jpg" +) +controlnet_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328)) + +prompt = "一只小狗，毛发光洁柔顺，眼神灵动，背景是樱花纷飞的春日庭院，唯美温馨。" +image = pipe( + prompt, seed=0, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)] +) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py new file mode 100644 index 0000000000000000000000000000000000000000..ddbb0818e1d7c89a324a10236fe6037cd84bb146 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py @@ -0,0 +1,43 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput +from PIL import Image +import torch +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth", origin_file_pattern="model.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="depth/image_1.jpg" +) + +controlnet_image = Image.open("data/example_image_dataset/depth/image_1.jpg").resize((1328, 1328)) + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe( + prompt, seed=0, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)] +) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py new file mode 100644 index 0000000000000000000000000000000000000000..ffee149e110bdc0959044209e6737567298697fd --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py @@ -0,0 +1,44 @@ +import torch +from PIL import Image +from modelscope import dataset_snapshot_download +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint", origin_file_pattern="model.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="inpaint/*.jpg" +) +prompt = "a cat with sunglasses" +controlnet_image = Image.open("./data/example_image_dataset/inpaint/image_1.jpg").convert("RGB").resize((1328, 1328)) +inpaint_mask = Image.open("./data/example_image_dataset/inpaint/mask.jpg").convert("RGB").resize((1328, 1328)) +image = pipe( + prompt, seed=0, + input_image=controlnet_image, inpaint_mask=inpaint_mask, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image, inpaint_mask=inpaint_mask)], + num_inference_steps=40, +) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-InpaintCanny.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-InpaintCanny.py new file mode 100644 index 0000000000000000000000000000000000000000..38fdcc1199ec272a05f8c76b3697ec82e3adf28d --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-InpaintCanny.py @@ -0,0 +1,59 @@ +import torch +from PIL import Image +from modelscope import dataset_snapshot_download +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint", origin_file_pattern="model.safetensors", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny", origin_file_pattern="model.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="canny/*.jpg" +) +prompt = "一只小狗，毛发光洁柔顺，眼神灵动，背景是樱花纷飞的春日庭院，唯美温馨。" + +controlnet_canny_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328)) + +controlnet_inpaint_image = Image.open("./data/example_image_dataset/canny/image_2.jpg").convert("RGB").resize((1328, 1328)) +# generate a centered square mask +inpaint_mask = Image.new("L", controlnet_inpaint_image.size, 0) +mask_size = 512 +left = (controlnet_inpaint_image.width - mask_size) // 2 +top = (controlnet_inpaint_image.height - mask_size) // 2 +right = left + mask_size +bottom = top + mask_size +inpaint_mask.paste(255, (left, top, right, bottom)) +inpaint_mask = inpaint_mask.resize((1328, 1328)).convert("RGB") + +image = pipe( + prompt, seed=0, + input_image=controlnet_inpaint_image, inpaint_mask=inpaint_mask, + blockwise_controlnet_inputs=[ + ControlNetInput(image=controlnet_inpaint_image, inpaint_mask=inpaint_mask, controlnet_id=0), + ControlNetInput(image=controlnet_canny_image, controlnet_id=1), + ], + num_inference_steps=40, +) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-DMD2.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-DMD2.py new file mode 100644 index 0000000000000000000000000000000000000000..6b9566742cbdffea694613d353ef2c8f30471a41 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-DMD2.py @@ -0,0 +1,36 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth.core import load_state_dict +from modelscope import snapshot_download +import torch, math + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, # bfloat16 is recommended. + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, # bfloat16 is recommended. + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) + +snapshot_download("MusePublic/Qwen-Image-Distill", allow_file_pattern="qwen_image_distill_3step.safetensors", cache_dir="models") +lora_state_dict = load_state_dict("models/MusePublic/Qwen-Image-Distill/qwen_image_distill_3step.safetensors", device="cuda", torch_dtype=torch.bfloat16) +lora_state_dict = {i.replace("base_model.model.", "").replace(".weight", ".default.weight"): j for i, j in lora_state_dict.items()} +pipe.load_lora(pipe.dit, state_dict=lora_state_dict, hotload=True) + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt, seed=0, num_inference_steps=3, cfg_scale=1, exponential_shift_mu=math.log(2.5)) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py new file mode 100644 index 0000000000000000000000000000000000000000..8f99e1c2e2c96e30e566622bf70b19092b075513 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py @@ -0,0 +1,28 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Distill-Full", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt, seed=0, num_inference_steps=15, cfg_scale=1) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py new file mode 100644 index 0000000000000000000000000000000000000000..8f8f2323131b8c9a658986d7d7e2e6e6e4f3fa1e --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py @@ -0,0 +1,31 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from modelscope import snapshot_download +import torch + +snapshot_download("DiffSynth-Studio/Qwen-Image-Distill-LoRA", local_dir="models/DiffSynth-Studio/Qwen-Image-Distill-LoRA") +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-Distill-LoRA/model.safetensors", hotload=True) + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt, seed=0, num_inference_steps=15, cfg_scale=1) +image.save("image.jpg") \ No newline at end of file diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py new file mode 100644 index 0000000000000000000000000000000000000000..97357aae623e71a806d992dd82e8adc6817f963e --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py @@ -0,0 +1,43 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from PIL import Image +import torch + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit-2509", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) + +image_1 = pipe(prompt="一位少女", seed=0, num_inference_steps=40, height=1328, width=1024) +image_1.save("image1.jpg") + +image_2 = pipe(prompt="一位老人", seed=0, num_inference_steps=40, height=1328, width=1024) +image_2.save("image2.jpg") + +prompt = "生成这两个人的合影" +edit_image = [Image.open("image1.jpg"), Image.open("image2.jpg")] +image_3 = pipe(prompt, edit_image=edit_image, seed=1, num_inference_steps=40, height=1328, width=1024, edit_image_auto_resize=True) +image_3.save("image3.jpg") + +# Qwen-Image-Edit-2509 is a multi-image editing model. +# Please use a list to input `edit_image`, even if the input contains only one image. +# edit_image = [Image.open("image.jpg")] +# Please do not input the image directly. +# edit_image = Image.open("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511-ICEdit.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511-ICEdit.py new file mode 100644 index 0000000000000000000000000000000000000000..8a90f6e793d13fe0ba61a46c616d5371a8d44e4f --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511-ICEdit.py @@ -0,0 +1,58 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from modelscope import snapshot_download +from PIL import Image +import torch + +# Load models +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit-2511", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) + +lora = ModelConfig( + model_id="DiffSynth-Studio/Qwen-Image-Edit-2511-ICEdit-LoRA", + origin_file_pattern="model.safetensors" +) +pipe.load_lora(pipe.dit, lora) + +# Load images +snapshot_download( + "DiffSynth-Studio/Qwen-Image-Edit-2511-ICEdit-LoRA", + local_dir="./data", + allow_file_pattern="assets/*" +) +edit_image = [ + Image.open("data/assets/image1_original.png"), + Image.open("data/assets/image1_edit_1.png"), + Image.open("data/assets/image2_original.png") +] +prompt = "Edit image 3 based on the transformation from image 1 to image 2." +negative_prompt = "泛黄，AI感，不真实，丑陋，油腻的皮肤，异常的肢体，不协调的肢体" + +# Generate +image_4 = pipe( + prompt=prompt, negative_prompt=negative_prompt, + edit_image=edit_image, + seed=1, + num_inference_steps=50, + height=1280, + width=720, + zero_cond_t=True, +) +image_4.save("image.png") \ No newline at end of file diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511-Lightning.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511-Lightning.py new file mode 100644 index 0000000000000000000000000000000000000000..cd5b4f385fbf89ab6192069dd1c36bf012eda713 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511-Lightning.py @@ -0,0 +1,63 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, FlowMatchScheduler +from modelscope import dataset_snapshot_download +from PIL import Image +import torch + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit-2511", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) + +lora = ModelConfig( + model_id="lightx2v/Qwen-Image-Edit-2511-Lightning", + origin_file_pattern="Qwen-Image-Edit-2511-Lightning-4steps-V1.0-bf16.safetensors" +) +pipe.load_lora(pipe.dit, lora, alpha=1) +pipe.scheduler = FlowMatchScheduler("Qwen-Image-Lightning") + + +dataset_snapshot_download( + "DiffSynth-Studio/example_image_dataset", + allow_file_pattern="qwen_image_edit/*", + local_dir="data/example_image_dataset", +) + +prompt = "生成这两个人的合影" +edit_image = [ + Image.open("data/example_image_dataset/qwen_image_edit/image1.jpg"), + Image.open("data/example_image_dataset/qwen_image_edit/image2.jpg"), +] +image = pipe( + prompt, + edit_image=edit_image, + seed=1, + num_inference_steps=4, + height=1152, + width=896, + edit_image_auto_resize=True, + zero_cond_t=True, # This is a special parameter introduced by Qwen-Image-Edit-2511 + cfg_scale=1.0, +) +image.save("image.jpg") + +# Qwen-Image-Edit-2511 is a multi-image editing model. +# Please use a list to input `edit_image`, even if the input contains only one image. +# edit_image = [Image.open("image.jpg")] +# Please do not input the image directly. +# edit_image = Image.open("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511.py new file mode 100644 index 0000000000000000000000000000000000000000..a5e78b4919e1ef0d6a679215e60ad5b26dacce30 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511.py @@ -0,0 +1,54 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from modelscope import dataset_snapshot_download +from PIL import Image +import torch + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit-2511", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) + +dataset_snapshot_download( + "DiffSynth-Studio/example_image_dataset", + allow_file_pattern="qwen_image_edit/*", + local_dir="data/example_image_dataset", +) + +prompt = "生成这两个人的合影" +edit_image = [ + Image.open("data/example_image_dataset/qwen_image_edit/image1.jpg"), + Image.open("data/example_image_dataset/qwen_image_edit/image2.jpg"), +] +image = pipe( + prompt, + edit_image=edit_image, + seed=1, + num_inference_steps=40, + height=1152, + width=896, + edit_image_auto_resize=True, + zero_cond_t=True, # This is a special parameter introduced by Qwen-Image-Edit-2511 +) +image.save("image.jpg") + +# Qwen-Image-Edit-2511 is a multi-image editing model. +# Please use a list to input `edit_image`, even if the input contains only one image. +# edit_image = [Image.open("image.jpg")] +# Please do not input the image directly. +# edit_image = Image.open("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py new file mode 100644 index 0000000000000000000000000000000000000000..0a849ca8bb6a69cf7aec3b7eb92253d82c6c7cf1 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py @@ -0,0 +1,37 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch +from modelscope import snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) +snapshot_download("DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix", local_dir="models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix", allow_file_pattern="model.safetensors") +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix/model.safetensors", hotload=True) + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt=prompt, seed=0, num_inference_steps=40, height=1024, width=768) +image.save("image.jpg") + +prompt = "将裙子变成粉色" +image = image.resize((512, 384)) +image = pipe(prompt, edit_image=image, seed=1, num_inference_steps=40, height=1024, width=768, edit_rope_interpolation=True, edit_image_auto_resize=False) +image.save(f"image2.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py new file mode 100644 index 0000000000000000000000000000000000000000..51193663106ae82b42d71d3ad7c301b39b8aec81 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py @@ -0,0 +1,37 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +input_image = pipe(prompt=prompt, seed=0, num_inference_steps=40, height=1328, width=1024) +input_image.save("image1.jpg") + +prompt = "将裙子改为粉色" +# edit_image_auto_resize=True: auto resize input image to match the area of 1024*1024 with the original aspect ratio +image = pipe(prompt, edit_image=input_image, seed=1, num_inference_steps=40, height=1328, width=1024, edit_image_auto_resize=True) +image.save(f"image2.jpg") + +# edit_image_auto_resize=False: do not resize input image +image = pipe(prompt, edit_image=input_image, seed=1, num_inference_steps=40, height=1328, width=1024, edit_image_auto_resize=False) +image.save(f"image3.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py new file mode 100644 index 0000000000000000000000000000000000000000..276aaa204d89acdb3554bfc7b91fa6418f751380 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py @@ -0,0 +1,125 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch +from PIL import Image, ImageDraw, ImageFont +from modelscope import dataset_snapshot_download, snapshot_download +import random + + +def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False): + # Create a blank image for overlays + overlay = Image.new('RGBA', image.size, (0, 0, 0, 0)) + + colors = [ + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + ] + # Generate random colors for each mask + if use_random_colors: + colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))] + + # Font settings + try: + font = ImageFont.truetype("wqy-zenhei.ttc", font_size) # Adjust as needed + except IOError: + font = ImageFont.load_default(font_size) + + # Overlay each mask onto the overlay image + for mask, mask_prompt, color in zip(masks, mask_prompts, colors): + # Convert mask to RGBA mode + mask_rgba = mask.convert('RGBA') + mask_data = mask_rgba.getdata() + new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data] + mask_rgba.putdata(new_data) + + # Draw the mask prompt text on the mask + draw = ImageDraw.Draw(mask_rgba) + mask_bbox = mask.getbbox() # Get the bounding box of the mask + text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position + draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font) + + # Alpha composite the overlay with this mask + overlay = Image.alpha_composite(overlay, mask_rgba) + + # Composite the overlay onto the original image + result = Image.alpha_composite(image.convert('RGBA'), overlay) + + # Save or display the resulting image + result.save(output_path) + + return result + + +def example(pipe, seeds, example_id, global_prompt, entity_prompts, height=784, width=1280): + dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/eligen/poster/example_{example_id}/*.png" + ) + masks = [ + Image.open(f"./data/examples/eligen/poster/example_{example_id}/{i}.png").convert('RGB').resize((width, height)) + for i in range(len(entity_prompts)) + ] + negative_prompt = "网格化，规则的网格，模糊, 低分辨率, 低质量, 变形, 畸形, 错误的解剖学, 变形的手, 变形的身体, 变形的脸, 变形的头发, 变形的眼睛, 变形的嘴巴" + for seed in seeds: + # generate image + image = pipe( + prompt=global_prompt, + cfg_scale=4.0, + negative_prompt=negative_prompt, + num_inference_steps=40, + seed=seed, + height=height, + width=width, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks, + ) + image.save(f"eligen_poster_example_{example_id}_{seed}.png") + image = Image.new("RGB", (width, height), (0, 0, 0)) + visualize_masks(image, masks, entity_prompts, f"eligen_poster_example_{example_id}_mask_{seed}.png") + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) +snapshot_download( + "DiffSynth-Studio/Qwen-Image-EliGen-Poster", + local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen-Poster", + allow_file_pattern="model.safetensors", +) +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen-Poster/model.safetensors", hotload=True) +global_prompt = "一张以柔粉紫为背景的海报，左侧有大号粉紫色文字“Qwen-Image EliGen-Poster”，粉紫色椭圆框内白色小字：“图像精确分区控制模型”。右侧有一只小兔子在拆礼物，旁边站着一只头顶迷你烟花发射器的小龙（卡通Q版）。背景有一些白云点缀。整体风格卡通可爱，传达节日惊喜的主题。" +entity_prompts = ["粉紫色文字“Qwen-Image EliGen-Poster”", "粉紫色椭圆框内白色小字：“图像精确分区控制模型”", "一只小兔子在拆礼物，小兔子旁边站着一只头顶迷你烟花发射器的小龙（卡通Q版）"] +seed = [42] +example(pipe, seed, 1, global_prompt, entity_prompts) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py new file mode 100644 index 0000000000000000000000000000000000000000..353ff19edc96b701aebcd4610e7edfe4025a90f3 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py @@ -0,0 +1,117 @@ +import torch +import random +from PIL import Image, ImageDraw, ImageFont +from modelscope import dataset_snapshot_download, snapshot_download +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig + +def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False): + # Create a blank image for overlays + overlay = Image.new('RGBA', image.size, (0, 0, 0, 0)) + + colors = [ + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + ] + # Generate random colors for each mask + if use_random_colors: + colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))] + + # Font settings + try: + font = ImageFont.truetype("wqy-zenhei.ttc", font_size) # Adjust as needed + except IOError: + font = ImageFont.load_default(font_size) + + # Overlay each mask onto the overlay image + for mask, mask_prompt, color in zip(masks, mask_prompts, colors): + # Convert mask to RGBA mode + mask_rgba = mask.convert('RGBA') + mask_data = mask_rgba.getdata() + new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data] + mask_rgba.putdata(new_data) + + # Draw the mask prompt text on the mask + draw = ImageDraw.Draw(mask_rgba) + mask_bbox = mask.getbbox() # Get the bounding box of the mask + text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position + draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font) + + # Alpha composite the overlay with this mask + overlay = Image.alpha_composite(overlay, mask_rgba) + + # Composite the overlay onto the original image + result = Image.alpha_composite(image.convert('RGBA'), overlay) + + # Save or display the resulting image + result.save(output_path) + + return result + +def example(pipe, seeds, example_id, global_prompt, entity_prompts): + dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/qwen-image/example_{example_id}/*.png") + masks = [Image.open(f"./data/examples/eligen/qwen-image/example_{example_id}/{i}.png").convert('RGB').resize((1024, 1024)) for i in range(len(entity_prompts))] + negative_prompt = "网格化，规则的网格，模糊, 低分辨率, 低质量, 变形, 畸形, 错误的解剖学, 变形的手, 变形的身体, 变形的脸, 变形的头发, 变形的眼睛, 变形的嘴巴" + for seed in seeds: + # generate image + image = pipe( + prompt=global_prompt, + cfg_scale=4.0, + negative_prompt=negative_prompt, + num_inference_steps=40, + seed=seed, + height=1024, + width=1024, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks, + ) + image.save(f"eligen_example_{example_id}_{seed}.png") + visualize_masks(image, masks, entity_prompts, f"eligen_example_{example_id}_mask_{seed}.png") + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) +snapshot_download("DiffSynth-Studio/Qwen-Image-EliGen-V2", local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen-V2", allow_file_pattern="model.safetensors") +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen-V2/model.safetensors", hotload=True) + +seeds = [0] + +global_prompt = "写实摄影风格. A beautiful asia woman wearing white dress, she is holding a mirror with her right arm, with a beach background." +entity_prompts = ["A beautiful woman", "mirror", "necklace", "glasses", "earring", "white dress", "jewelry headpiece"] +example(pipe, seeds, 7, global_prompt, entity_prompts) + +global_prompt = "写实摄影风格, 细节丰富。街头一位漂亮的女孩，穿着衬衫和短裤，手持写有“实体控制”的标牌，背景是繁忙的城市街道，阳光明媚，行人匆匆。" +entity_prompts = ["一个漂亮的女孩", "标牌 '实体控制'", "短裤", "衬衫"] +example(pipe, seeds, 4, global_prompt, entity_prompts) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py new file mode 100644 index 0000000000000000000000000000000000000000..e2cbc2e17e3d9b27950f2169825b2873c6f8b1d4 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py @@ -0,0 +1,118 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch +from PIL import Image, ImageDraw, ImageFont +from modelscope import dataset_snapshot_download, snapshot_download +import random + + +def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False): + # Create a blank image for overlays + overlay = Image.new('RGBA', image.size, (0, 0, 0, 0)) + + colors = [ + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + ] + # Generate random colors for each mask + if use_random_colors: + colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))] + + # Font settings + try: + font = ImageFont.truetype("wqy-zenhei.ttc", font_size) # Adjust as needed + except IOError: + font = ImageFont.load_default(font_size) + + # Overlay each mask onto the overlay image + for mask, mask_prompt, color in zip(masks, mask_prompts, colors): + # Convert mask to RGBA mode + mask_rgba = mask.convert('RGBA') + mask_data = mask_rgba.getdata() + new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data] + mask_rgba.putdata(new_data) + + # Draw the mask prompt text on the mask + draw = ImageDraw.Draw(mask_rgba) + mask_bbox = mask.getbbox() # Get the bounding box of the mask + text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position + draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font) + + # Alpha composite the overlay with this mask + overlay = Image.alpha_composite(overlay, mask_rgba) + + # Composite the overlay onto the original image + result = Image.alpha_composite(image.convert('RGBA'), overlay) + + # Save or display the resulting image + result.save(output_path) + + return result + +def example(pipe, seeds, example_id, global_prompt, entity_prompts): + dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/qwen-image/example_{example_id}/*.png") + masks = [Image.open(f"./data/examples/eligen/qwen-image/example_{example_id}/{i}.png").convert('RGB') for i in range(len(entity_prompts))] + negative_prompt = "" + for seed in seeds: + # generate image + image = pipe( + prompt=global_prompt, + cfg_scale=4.0, + negative_prompt=negative_prompt, + num_inference_steps=30, + seed=seed, + height=1024, + width=1024, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks, + ) + image.save(f"eligen_example_{example_id}_{seed}.png") + visualize_masks(image, masks, entity_prompts, f"eligen_example_{example_id}_mask_{seed}.png") + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) +snapshot_download("DiffSynth-Studio/Qwen-Image-EliGen", local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen", allow_file_pattern="model.safetensors") +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen/model.safetensors", hotload=True) + +# example 1 +global_prompt = "A breathtaking beauty of Raja Ampat by the late-night moonlight , one beautiful woman from behind wearing a pale blue long dress with soft glow, sitting at the top of a cliff looking towards the beach,pastell light colors, a group of small distant birds flying in far sky, a boat sailing on the sea, best quality, realistic, whimsical, fantastic, splash art, intricate detailed, hyperdetailed, maximalist style, photorealistic, concept art, sharp focus, harmony, serenity, tranquility, soft pastell colors,ambient occlusion, cozy ambient lighting, masterpiece, liiv1, linquivera, metix, mentixis, masterpiece, award winning, view from above\n" +entity_prompts = ["cliff", "sea", "moon", "sailing boat", "a seated beautiful woman", "pale blue long dress with soft glow"] +example(pipe, [0], 1, global_prompt, entity_prompts) + +# example 2 +global_prompt = "samurai girl wearing a kimono, she's holding a sword glowing with red flame, her long hair is flowing in the wind, she is looking at a small bird perched on the back of her hand. ultra realist style. maximum image detail. maximum realistic render." +entity_prompts = ["flowing hair", "sword glowing with red flame", "A cute bird", "yellow belt"] +example(pipe, [0], 2, global_prompt, entity_prompts) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py new file mode 100644 index 0000000000000000000000000000000000000000..f0f5941899e24430ddede016eb8b8d9d0307d36e --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py @@ -0,0 +1,46 @@ +from PIL import Image +import torch +from modelscope import dataset_snapshot_download, snapshot_download +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth.utils.controlnet import Annotator + +allow_file_pattern = ["sk_model.pth", "sk_model2.pth", "dpt_hybrid-midas-501f0c75.pt", "ControlNetHED.pth", "body_pose_model.pth", "hand_pose_model.pth", "facenet.pth", "scannet.pt"] +snapshot_download("lllyasviel/Annotators", local_dir="models/Annotators", allow_file_pattern=allow_file_pattern) + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) +snapshot_download("DiffSynth-Studio/Qwen-Image-In-Context-Control-Union", local_dir="models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union", allow_file_pattern="model.safetensors") +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union/model.safetensors", hotload=True) + +dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/qwen-image-context-control/image.jpg") +origin_image = Image.open("data/examples/qwen-image-context-control/image.jpg").resize((1024, 1024)) +annotator_ids = ['openpose', 'canny', 'depth', 'lineart', 'softedge', 'normal'] +for annotator_id in annotator_ids: + annotator = Annotator(processor_id=annotator_id, device="cuda") + control_image = annotator(origin_image) + control_image.save(f"{annotator.processor_id}.png") + + control_prompt = "Context_Control. " + prompt = f"{control_prompt}一个穿着淡蓝色的漂亮女孩正在翩翩起舞，背景是梦幻的星空，光影交错，细节精致。" + negative_prompt = "网格化，规则的网格，模糊, 低分辨率, 低质量, 变形, 畸形, 错误的解剖学, 变形的手, 变形的身体, 变形的脸, 变形的头发, 变形的眼睛, 变形的嘴巴" + image = pipe(prompt, seed=1, negative_prompt=negative_prompt, context_image=control_image, height=1024, width=1024) + image.save(f"image_{annotator.processor_id}.png") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control-V2.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control-V2.py new file mode 100644 index 0000000000000000000000000000000000000000..70b0fcdb5a44443c7ab25dc3aaec165969a4b6c6 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control-V2.py @@ -0,0 +1,54 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from modelscope import dataset_snapshot_download +from PIL import Image +import torch + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Layered-Control", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Layered-Control-V2", origin_file_pattern="model.safetensors", **vram_config)) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="layer_v2/*.png" +) + +prompt = "Text 'APRIL'" +input_image = Image.open("data/example_image_dataset/layer_v2/image_1.png").convert("RGBA").resize((1024, 1024)) +image = pipe( + prompt, seed=0, + height=1024, width=1024, + layer_input_image=input_image, layer_num=0, + num_inference_steps=10, cfg_scale=4, +) +image[0].save("image_prompt.png") + +mask_image = Image.open("data/example_image_dataset/layer_v2/mask_2.png").convert("RGBA").resize((1024, 1024)) +input_image = Image.open("data/example_image_dataset/layer_v2/image_2.png").convert("RGBA").resize((1024, 1024)) +image = pipe( + prompt, seed=0, + height=1024, width=1024, + layer_input_image=input_image, layer_num=0, + context_image=mask_image, + num_inference_steps=10, cfg_scale=1.0, +) +image[0].save("image_mask.png") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..eb5c77d2cc47984f7b798ca6b5dda7d9799d3431 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control.py @@ -0,0 +1,44 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from modelscope import snapshot_download +from PIL import Image +import torch + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Layered-Control", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) + +snapshot_download( + model_id="DiffSynth-Studio/Qwen-Image-Layered-Control", + allow_file_pattern="assets/image_1_input.png", + local_dir="data/layered_input" +) + +prompt = "A cartoon skeleton character wearing a purple hat and holding a gift box" +input_image = Image.open("data/layered_input/assets/image_1_input.png").convert("RGBA").resize((1024, 1024)) +images = pipe( + prompt, + seed=0, + num_inference_steps=30, cfg_scale=4, + height=1024, width=1024, + layer_input_image=input_image, + layer_num=0, +) +images[0].save("image.png") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered.py new file mode 100644 index 0000000000000000000000000000000000000000..4f3438d5f0c59416a5755b30bb31d67eb8a411a3 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered.py @@ -0,0 +1,46 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from modelscope import dataset_snapshot_download +from PIL import Image +import torch + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) + +dataset_snapshot_download( + "DiffSynth-Studio/example_image_dataset", + allow_patterns="layer/image.png", + local_dir="data/example_image_dataset" +) + +# Prompt should be provided to the pipeline. Our pipeline will not generate the prompt. +prompt = 'A cheerful child with brown hair is waving enthusiastically under a bright blue sky filled with colorful confetti and balloons. The word "HELLO!" is prominently displayed in bold red letters above the child, while "Have a Great Day!" appears in elegant cursive at the bottom right corner. The scene is vibrant and festive, with a mix of pastel colors and dynamic shapes creating a joyful atmosphere.' +# Height and width should be consistent with input_image and be divided evenly by 16 +input_image = Image.open("data/example_image_dataset/layer/image.png").convert("RGBA").resize((864, 480)) +images = pipe( + prompt, + seed=1, num_inference_steps=50, + height=480, width=864, + layer_input_image=input_image, layer_num=3, +) +for i, image in enumerate(images): + if i == 0: continue # The first image is the input image. + image.save(f"image_{i}.png") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-i2L.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-i2L.py new file mode 100644 index 0000000000000000000000000000000000000000..b91d606b0125a35f90752b5a84b3ba0af4a5daf2 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image-i2L.py @@ -0,0 +1,134 @@ +from diffsynth.pipelines.qwen_image import ( + QwenImagePipeline, ModelConfig, + QwenImageUnit_Image2LoRAEncode, QwenImageUnit_Image2LoRADecode +) +from diffsynth.utils.lora import merge_lora +from diffsynth import load_state_dict +from modelscope import snapshot_download +from safetensors.torch import save_file +import torch +from PIL import Image + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +vram_config_disk_offload = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": "disk", + "onload_device": "disk", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} + +def demo_style(): + # Load models + pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="SigLIP2-G384/model.safetensors", **vram_config_disk_offload), + ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="DINOv3-7B/model.safetensors", **vram_config_disk_offload), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-i2L", origin_file_pattern="Qwen-Image-i2L-Style.safetensors", **vram_config_disk_offload), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, + ) + + # Load images + snapshot_download( + model_id="DiffSynth-Studio/Qwen-Image-i2L", + allow_file_pattern="assets/style/1/*", + local_dir="data/examples" + ) + images = [ + Image.open("data/examples/assets/style/1/0.jpg"), + Image.open("data/examples/assets/style/1/1.jpg"), + Image.open("data/examples/assets/style/1/2.jpg"), + Image.open("data/examples/assets/style/1/3.jpg"), + Image.open("data/examples/assets/style/1/4.jpg"), + ] + + # Model inference + with torch.no_grad(): + embs = QwenImageUnit_Image2LoRAEncode().process(pipe, image2lora_images=images) + lora = QwenImageUnit_Image2LoRADecode().process(pipe, **embs)["lora"] + save_file(lora, "model_style.safetensors") + + +def demo_coarse_fine_bias(): + # Load models + pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config_disk_offload), + ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="SigLIP2-G384/model.safetensors", **vram_config_disk_offload), + ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="DINOv3-7B/model.safetensors", **vram_config_disk_offload), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-i2L", origin_file_pattern="Qwen-Image-i2L-Coarse.safetensors", **vram_config_disk_offload), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-i2L", origin_file_pattern="Qwen-Image-i2L-Fine.safetensors", **vram_config_disk_offload), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, + ) + + # Load images + snapshot_download( + model_id="DiffSynth-Studio/Qwen-Image-i2L", + allow_file_pattern="assets/lora/3/*", + local_dir="data/examples" + ) + images = [ + Image.open("data/examples/assets/lora/3/0.jpg"), + Image.open("data/examples/assets/lora/3/1.jpg"), + Image.open("data/examples/assets/lora/3/2.jpg"), + Image.open("data/examples/assets/lora/3/3.jpg"), + Image.open("data/examples/assets/lora/3/4.jpg"), + Image.open("data/examples/assets/lora/3/5.jpg"), + ] + + # Model inference + with torch.no_grad(): + embs = QwenImageUnit_Image2LoRAEncode().process(pipe, image2lora_images=images) + lora = QwenImageUnit_Image2LoRADecode().process(pipe, **embs)["lora"] + lora_bias = ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-i2L", origin_file_pattern="Qwen-Image-i2L-Bias.safetensors") + lora_bias.download_if_necessary() + lora_bias = load_state_dict(lora_bias.path, torch_dtype=torch.bfloat16, device="cuda") + lora = merge_lora([lora, lora_bias]) + save_file(lora, "model_coarse_fine_bias.safetensors") + + +def generate_image(lora_path, prompt, seed): + pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, + ) + pipe.load_lora(pipe.dit, lora_path) + image = pipe(prompt, seed=seed, height=1024, width=1024, num_inference_steps=50) + return image + + +demo_style() +image = generate_image("model_style.safetensors", "a cat", 0) +image.save("image_1.jpg") + +demo_coarse_fine_bias() +image = generate_image("model_coarse_fine_bias.safetensors", "bowl", 1) +image.save("image_2.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image.py new file mode 100644 index 0000000000000000000000000000000000000000..aae4a22b332493c308735b5f86432edb24dfc6f2 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_inference_low_vram/Qwen-Image.py @@ -0,0 +1,28 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.float8_e4m3fn, + "onload_device": "cpu", + "preparing_dtype": torch.float8_e4m3fn, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt, seed=0, num_inference_steps=40) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/FireRed-Image-Edit-1.0.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/FireRed-Image-Edit-1.0.sh new file mode 100644 index 0000000000000000000000000000000000000000..b538f585819c4688f658d324c3c9d2d858387a6c --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/FireRed-Image-Edit-1.0.sh @@ -0,0 +1,17 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/FireRed-Image-Edit-1.0/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/FireRed-Image-Edit-1.0 \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/FireRed-Image-Edit-1.0/metadata.json \ + --data_file_keys "image,edit_image" \ + --extra_inputs "edit_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "FireRedTeam/FireRed-Image-Edit-1.0:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FireRed-Image-Edit-1.0_full" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/FireRed-Image-Edit-1.1.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/FireRed-Image-Edit-1.1.sh new file mode 100644 index 0000000000000000000000000000000000000000..d91cad87554eb996f00defb4b13689e06ee033e4 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/FireRed-Image-Edit-1.1.sh @@ -0,0 +1,17 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/FireRed-Image-Edit-1.1/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/FireRed-Image-Edit-1.1 \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/FireRed-Image-Edit-1.1/metadata.json \ + --data_file_keys "image,edit_image" \ + --extra_inputs "edit_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "FireRedTeam/FireRed-Image-Edit-1.1:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FireRed-Image-Edit-1.1_full" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-2512.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-2512.sh new file mode 100644 index 0000000000000000000000000000000000000000..836219c03e53fc3d593b2752197c676d4768c4b6 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-2512.sh @@ -0,0 +1,15 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-2512/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-2512 \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-2512/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image-2512:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-2512_full" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh new file mode 100644 index 0000000000000000000000000000000000000000..937f1899598e07dfd168dc05e138212a8c7b9762 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh @@ -0,0 +1,40 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Blockwise-ControlNet-Canny/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Blockwise-ControlNet-Canny \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Blockwise-ControlNet-Canny/metadata.csv \ + --data_file_keys "image,blockwise_controlnet_image" \ + --max_pixels 1048576 \ + --dataset_repeat 400 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors,DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny:model.safetensors" \ + --learning_rate 1e-3 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.blockwise_controlnet.models.0." \ + --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Canny_full" \ + --trainable_models "blockwise_controlnet" \ + --extra_inputs "blockwise_controlnet_image" \ + --use_gradient_checkpointing \ + --find_unused_parameters + +# If you want to pre-train a Blockwise ControlNet from scratch, +# please run the following script to first generate the initialized model weights file, +# and then start training with a high learning rate (1e-3). + +# python examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Initialize.py + +# accelerate launch examples/qwen_image/model_training/train.py \ +# --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Blockwise-ControlNet-Canny \ +# --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Blockwise-ControlNet-Canny/metadata.csv \ +# --data_file_keys "image,blockwise_controlnet_image" \ +# --max_pixels 1048576 \ +# --dataset_repeat 50 \ +# --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ +# --model_paths '["models/blockwise_controlnet.safetensors"]' \ +# --learning_rate 1e-3 \ +# --num_epochs 2 \ +# --remove_prefix_in_ckpt "pipe.blockwise_controlnet.models.0." \ +# --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Canny_full" \ +# --trainable_models "blockwise_controlnet" \ +# --extra_inputs "blockwise_controlnet_image" \ +# --use_gradient_checkpointing \ +# --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh new file mode 100644 index 0000000000000000000000000000000000000000..1c8c03fe45393421f27acb8210e80398e6157376 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh @@ -0,0 +1,40 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Blockwise-ControlNet-Depth/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Blockwise-ControlNet-Depth \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Blockwise-ControlNet-Depth/metadata.csv \ + --data_file_keys "image,blockwise_controlnet_image" \ + --max_pixels 1048576 \ + --dataset_repeat 400 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors,DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth:model.safetensors" \ + --learning_rate 1e-3 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.blockwise_controlnet.models.0." \ + --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Depth_full" \ + --trainable_models "blockwise_controlnet" \ + --extra_inputs "blockwise_controlnet_image" \ + --use_gradient_checkpointing \ + --find_unused_parameters + +# If you want to pre-train a Blockwise ControlNet from scratch, +# please run the following script to first generate the initialized model weights file, +# and then start training with a high learning rate (1e-3). + +# python examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Initialize.py + +# accelerate launch examples/qwen_image/model_training/train.py \ +# --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Blockwise-ControlNet-Depth \ +# --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Blockwise-ControlNet-Depth/metadata.csv \ +# --data_file_keys "image,blockwise_controlnet_image" \ +# --max_pixels 1048576 \ +# --dataset_repeat 50 \ +# --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ +# --model_paths '["models/blockwise_controlnet.safetensors"]' \ +# --learning_rate 1e-3 \ +# --num_epochs 2 \ +# --remove_prefix_in_ckpt "pipe.blockwise_controlnet.models.0." \ +# --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Depth_full" \ +# --trainable_models "blockwise_controlnet" \ +# --extra_inputs "blockwise_controlnet_image" \ +# --use_gradient_checkpointing \ +# --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh new file mode 100644 index 0000000000000000000000000000000000000000..610c3192218c4da563b735a2829b7048412a2974 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh @@ -0,0 +1,40 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Blockwise-ControlNet-Inpaint/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config.yaml examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Blockwise-ControlNet-Inpaint \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Blockwise-ControlNet-Inpaint/metadata.csv \ + --data_file_keys "image,blockwise_controlnet_image,blockwise_controlnet_inpaint_mask" \ + --max_pixels 1048576 \ + --dataset_repeat 400 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors,DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint:model.safetensors" \ + --learning_rate 1e-3 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.blockwise_controlnet.models.0." \ + --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Inpaint_full" \ + --trainable_models "blockwise_controlnet" \ + --extra_inputs "blockwise_controlnet_image,blockwise_controlnet_inpaint_mask" \ + --use_gradient_checkpointing \ + --find_unused_parameters + +# If you want to pre-train a Inpaint Blockwise ControlNet from scratch, +# please run the following script to first generate the initialized model weights file, +# and then start training with a high learning rate (1e-3). + +# python examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Inpaint-Initialize.py + +# accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config.yaml examples/qwen_image/model_training/train.py \ +# --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Blockwise-ControlNet-Inpaint \ +# --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Blockwise-ControlNet-Inpaint/metadata.csv \ +# --data_file_keys "image,blockwise_controlnet_image,blockwise_controlnet_inpaint_mask" \ +# --max_pixels 1048576 \ +# --dataset_repeat 50 \ +# --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ +# --model_paths '["models/blockwise_controlnet_inpaint.safetensors"]' \ +# --learning_rate 1e-3 \ +# --num_epochs 2 \ +# --remove_prefix_in_ckpt "pipe.blockwise_controlnet.models.0." \ +# --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Inpaint_full" \ +# --trainable_models "blockwise_controlnet" \ +# --extra_inputs "blockwise_controlnet_image,blockwise_controlnet_inpaint_mask" \ +# --use_gradient_checkpointing \ +# --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh new file mode 100644 index 0000000000000000000000000000000000000000..5b76c349d9cbce8ee8b026074b54a13c58d5c4d4 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh @@ -0,0 +1,15 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Distill-Full/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Distill-Full \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Distill-Full/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "DiffSynth-Studio/Qwen-Image-Distill-Full:diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Distill-Full_full" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh new file mode 100644 index 0000000000000000000000000000000000000000..78f87011782d1ac9ea6f6826e4fd6804a696dd8a --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh @@ -0,0 +1,17 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Edit-2509/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Edit-2509 \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Edit-2509/metadata.json \ + --data_file_keys "image,edit_image" \ + --extra_inputs "edit_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image-Edit-2509:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Edit-2509_full" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Edit-2511.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Edit-2511.sh new file mode 100644 index 0000000000000000000000000000000000000000..f5249211c6e5bec4a75a73e5aa6480426bf70d4c --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Edit-2511.sh @@ -0,0 +1,18 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Edit-2511/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Edit-2511 \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Edit-2511/metadata.json \ + --data_file_keys "image,edit_image" \ + --extra_inputs "edit_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image-Edit-2511:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Edit-2511_full" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --find_unused_parameters \ + --zero_cond_t # This is a special parameter introduced by Qwen-Image-Edit-2511. Please enable it for this model. diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh new file mode 100644 index 0000000000000000000000000000000000000000..a091c6ea2e0be11b33d95edb7512744f9e3fb0df --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh @@ -0,0 +1,17 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Edit/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Edit \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Edit/metadata.csv \ + --data_file_keys "image,edit_image" \ + --extra_inputs "edit_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image-Edit:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Edit_full" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Layered-Control.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Layered-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..b29760184fd5ed08acb597c0bad83a6598b23f99 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Layered-Control.sh @@ -0,0 +1,18 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Layered-Control/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Layered-Control \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Layered-Control/metadata.json \ + --data_file_keys "image,layer_input_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "DiffSynth-Studio/Qwen-Image-Layered-Control:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image-Layered:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Layered-Control_full" \ + --trainable_models "dit" \ + --extra_inputs "layer_num,layer_input_image" \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Layered.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Layered.sh new file mode 100644 index 0000000000000000000000000000000000000000..5c72b1003462a81daf81545aa331c183efa893de --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image-Layered.sh @@ -0,0 +1,18 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Layered/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Layered \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Layered/metadata.json \ + --data_file_keys "image,layer_input_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image-Layered:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image-Layered:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Layered_full" \ + --trainable_models "dit" \ + --extra_inputs "layer_num,layer_input_image" \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image.sh new file mode 100644 index 0000000000000000000000000000000000000000..32b0b20889d1761178ea7af59d6fe6c8e2795690 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/Qwen-Image.sh @@ -0,0 +1,15 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image_full" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/accelerate_config.yaml b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/accelerate_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..83280f73f315a32eccb065f351d66b4b2678759d --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/accelerate_config.yaml @@ -0,0 +1,22 @@ +compute_environment: LOCAL_MACHINE +debug: false +deepspeed_config: + gradient_accumulation_steps: 1 + offload_optimizer_device: none + offload_param_device: none + zero3_init_flag: false + zero_stage: 2 +distributed_type: DEEPSPEED +downcast_bf16: 'no' +enable_cpu_affinity: false +machine_rank: 0 +main_training_function: main +mixed_precision: bf16 +num_machines: 1 +num_processes: 8 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8a75f3d91eeae160409650b482e5383ac26b297b --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml @@ -0,0 +1,22 @@ +compute_environment: LOCAL_MACHINE +debug: false +deepspeed_config: + gradient_accumulation_steps: 1 + offload_optimizer_device: 'cpu' + offload_param_device: 'cpu' + zero3_init_flag: false + zero_stage: 2 +distributed_type: DEEPSPEED +downcast_bf16: 'no' +enable_cpu_affinity: false +machine_rank: 0 +main_training_function: main +mixed_precision: bf16 +num_machines: 1 +num_processes: 8 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/accelerate_config_zero3.yaml b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/accelerate_config_zero3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e6a8d273346c6e04e7ab97c8aa661914a0819a86 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/full/accelerate_config_zero3.yaml @@ -0,0 +1,23 @@ +compute_environment: LOCAL_MACHINE +debug: false +deepspeed_config: + gradient_accumulation_steps: 1 + offload_optimizer_device: none + offload_param_device: none + zero3_init_flag: true + zero3_save_16bit_model: true + zero_stage: 3 +distributed_type: DEEPSPEED +downcast_bf16: 'no' +enable_cpu_affinity: false +machine_rank: 0 +main_training_function: main +mixed_precision: bf16 +num_machines: 1 +num_processes: 8 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/FireRed-Image-Edit-1.0.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/FireRed-Image-Edit-1.0.sh new file mode 100644 index 0000000000000000000000000000000000000000..57963661aeee6ff96f1ab6335166ace05cf0cc6f --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/FireRed-Image-Edit-1.0.sh @@ -0,0 +1,20 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/FireRed-Image-Edit-1.0/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/FireRed-Image-Edit-1.0 \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/FireRed-Image-Edit-1.0/metadata.json \ + --data_file_keys "image,edit_image" \ + --extra_inputs "edit_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "FireRedTeam/FireRed-Image-Edit-1.0:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FireRed-Image-Edit-1.0_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/FireRed-Image-Edit-1.1.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/FireRed-Image-Edit-1.1.sh new file mode 100644 index 0000000000000000000000000000000000000000..a7a24bbf573ef0999dab640de7d2b8cf45d0e75d --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/FireRed-Image-Edit-1.1.sh @@ -0,0 +1,20 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/FireRed-Image-Edit-1.1/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/FireRed-Image-Edit-1.1 \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/FireRed-Image-Edit-1.1/metadata.json \ + --data_file_keys "image,edit_image" \ + --extra_inputs "edit_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "FireRedTeam/FireRed-Image-Edit-1.1:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FireRed-Image-Edit-1.1_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-2512.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-2512.sh new file mode 100644 index 0000000000000000000000000000000000000000..fb96cd1fb197b89a6ca50d0eae4eb01985930bfb --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-2512.sh @@ -0,0 +1,18 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-2512/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-2512 \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-2512/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image-2512:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-2512_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh new file mode 100644 index 0000000000000000000000000000000000000000..3f4ef4ac9a083bf6127eb9ef6feb0dbfe582c142 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh @@ -0,0 +1,19 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Blockwise-ControlNet-Canny/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Blockwise-ControlNet-Canny \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Blockwise-ControlNet-Canny/metadata.csv \ + --data_file_keys "image,blockwise_controlnet_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors,DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny:model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Canny_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --extra_inputs "blockwise_controlnet_image" \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh new file mode 100644 index 0000000000000000000000000000000000000000..99b1b7840a01085361c4a430faedb6c774a4c41c --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh @@ -0,0 +1,19 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Blockwise-ControlNet-Depth/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Blockwise-ControlNet-Depth \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Blockwise-ControlNet-Depth/metadata.csv \ + --data_file_keys "image,blockwise_controlnet_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors,DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth:model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Depth_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --extra_inputs "blockwise_controlnet_image" \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh new file mode 100644 index 0000000000000000000000000000000000000000..cc4b95e318c74d7cbbbbf31b356472dd74cac440 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh @@ -0,0 +1,19 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Blockwise-ControlNet-Inpaint/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Blockwise-ControlNet-Inpaint \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Blockwise-ControlNet-Inpaint/metadata.csv \ + --data_file_keys "image,blockwise_controlnet_image,blockwise_controlnet_inpaint_mask" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors,DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint:model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Inpaint_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --extra_inputs "blockwise_controlnet_image,blockwise_controlnet_inpaint_mask" \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh new file mode 100644 index 0000000000000000000000000000000000000000..4e10c2bb7179491aa966b7cd266fd0a44bb4ad68 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh @@ -0,0 +1,17 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Distill-Full/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Distill-Full \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Distill-Full/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "DiffSynth-Studio/Qwen-Image-Distill-Full:diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Distill-Full_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh new file mode 100644 index 0000000000000000000000000000000000000000..1efd4715c030e93e9895acbbd57de1d199032cfa --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh @@ -0,0 +1,26 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Distill-LoRA/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Distill-LoRA \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Distill-LoRA/metadata.csv \ + --data_file_keys "image" \ + --extra_inputs "seed,rand_device,num_inference_steps,cfg_scale" \ + --height 1328 \ + --width 1328 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Distill-LoRA_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters \ + --task direct_distill + +# This is an experimental training feature designed to directly distill the model, enabling generation results with fewer steps to approximate those achieved with more steps. +# The model (https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA) is trained using this script. +# The sample dataset is provided solely to demonstrate the dataset format. For actual usage, please construct a larger dataset using the base model. diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh new file mode 100644 index 0000000000000000000000000000000000000000..d932bf8b48ee7a78d8a057636dbc784d1435858a --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh @@ -0,0 +1,20 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Edit-2509/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Edit-2509 \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Edit-2509/metadata.json \ + --data_file_keys "image,edit_image" \ + --extra_inputs "edit_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image-Edit-2509:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Edit-2509_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2511.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2511.sh new file mode 100644 index 0000000000000000000000000000000000000000..111d75b5ecb6daf7d7666d6e31cd7622e2fd8fbf --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2511.sh @@ -0,0 +1,21 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Edit-2511/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Edit-2511 \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Edit-2511/metadata.json \ + --data_file_keys "image,edit_image" \ + --extra_inputs "edit_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image-Edit-2511:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Edit-2511_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters \ + --zero_cond_t # This is a special parameter introduced by Qwen-Image-Edit-2511. Please enable it for this model. diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh new file mode 100644 index 0000000000000000000000000000000000000000..00c12a5b8f938e324be8817c880e732ce23d0cf8 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh @@ -0,0 +1,20 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Edit/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Edit \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Edit/metadata.csv \ + --data_file_keys "image,edit_image" \ + --extra_inputs "edit_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image-Edit:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Edit_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh new file mode 100644 index 0000000000000000000000000000000000000000..b30e0fa3ef00f8277a0472fd0b015ab12e1b9310 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh @@ -0,0 +1,20 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-EliGen-Poster/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-EliGen-Poster \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-EliGen-Poster/metadata.json \ + --data_file_keys "image,eligen_entity_masks" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-EliGen-Poster_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --extra_inputs "eligen_entity_masks,eligen_entity_prompts" \ + --use_gradient_checkpointing \ + --find_unused_parameters \ + --lora_checkpoint "models/DiffSynth-Studio/Qwen-Image-EliGen-V2/model.safetensors" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh new file mode 100644 index 0000000000000000000000000000000000000000..25a61e9941899fa2e378482d2490947b5ae492ba --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh @@ -0,0 +1,19 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-EliGen/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-EliGen \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-EliGen/metadata.json \ + --data_file_keys "image,eligen_entity_masks" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-EliGen_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --extra_inputs "eligen_entity_masks,eligen_entity_prompts" \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh new file mode 100644 index 0000000000000000000000000000000000000000..609d7fa6b6b716b71b5b105661121226a65cace0 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh @@ -0,0 +1,22 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-In-Context-Control-Union/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-In-Context-Control-Union \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-In-Context-Control-Union/metadata.csv \ + --data_file_keys "image,context_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-In-Context-Control-Union_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 64 \ + --lora_checkpoint "models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union/model.safetensors" \ + --extra_inputs "context_image" \ + --use_gradient_checkpointing \ + --find_unused_parameters + +# if you want to train from scratch, you can remove the --lora_checkpoint argument diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Layered-Control-V2.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Layered-Control-V2.sh new file mode 100644 index 0000000000000000000000000000000000000000..8531a87e21e0d2fe84687205066749b92dbcbba9 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Layered-Control-V2.sh @@ -0,0 +1,20 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Layered-Control-V2/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Layered-Control-V2 \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Layered-Control-V2/metadata.json \ + --data_file_keys "image,layer_input_image,context_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "DiffSynth-Studio/Qwen-Image-Layered-Control:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image-Layered:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Layered-Control-V2_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 64 \ + --extra_inputs "layer_num,layer_input_image,context_image" \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Layered-Control.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Layered-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..bb078aa92008cf997dc342a6f6822629ed6cd7a7 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Layered-Control.sh @@ -0,0 +1,20 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Layered-Control/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Layered-Control \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Layered-Control/metadata.json \ + --data_file_keys "image,layer_input_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "DiffSynth-Studio/Qwen-Image-Layered-Control:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image-Layered:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Layered-Control_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --extra_inputs "layer_num,layer_input_image" \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Layered.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Layered.sh new file mode 100644 index 0000000000000000000000000000000000000000..7b9be658c7fd4f79f57e021434a1283f54c793da --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image-Layered.sh @@ -0,0 +1,20 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Layered/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Layered \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Layered/metadata.json \ + --data_file_keys "image,layer_input_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image-Layered:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image-Layered:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Layered_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --extra_inputs "layer_num,layer_input_image" \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image.sh new file mode 100644 index 0000000000000000000000000000000000000000..dd15c54bb40d08a745ddc370647c0db0023685c7 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/lora/Qwen-Image.sh @@ -0,0 +1,18 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Initialize.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Initialize.py new file mode 100644 index 0000000000000000000000000000000000000000..5b0392f589d7739197ba4251bb1c6bfb0fcfe498 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Initialize.py @@ -0,0 +1,13 @@ +# This script is for initializing a Qwen-Image-Blockwise-ControlNet +from diffsynth import hash_state_dict_keys +from diffsynth.models.qwen_image_controlnet import QwenImageBlockWiseControlNet +import torch +from safetensors.torch import save_file + + +controlnet = QwenImageBlockWiseControlNet().to(dtype=torch.bfloat16, device="cuda") +controlnet.init_weight() +state_dict_controlnet = controlnet.state_dict() + +print(hash_state_dict_keys(state_dict_controlnet)) +save_file(state_dict_controlnet, "models/blockwise_controlnet.safetensors") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Inpaint-Initialize.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Inpaint-Initialize.py new file mode 100644 index 0000000000000000000000000000000000000000..83111894709eda93fbab925ca1804e2a3fd477dd --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Inpaint-Initialize.py @@ -0,0 +1,12 @@ +# This script is for initializing a Inpaint Qwen-Image-ControlNet +import torch +from diffsynth import hash_state_dict_keys +from diffsynth.models.qwen_image_controlnet import QwenImageBlockWiseControlNet +from safetensors.torch import save_file + +controlnet = QwenImageBlockWiseControlNet(additional_in_dim=4).to(dtype=torch.bfloat16, device="cuda") +controlnet.init_weight() +state_dict_controlnet = controlnet.state_dict() + +print(hash_state_dict_keys(state_dict_controlnet)) +save_file(state_dict_controlnet, "models/blockwise_controlnet_inpaint.safetensors") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/differential_training/Qwen-Image-LoRA.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/differential_training/Qwen-Image-LoRA.sh new file mode 100644 index 0000000000000000000000000000000000000000..d8eb7f5706b58bd093ae51c2c4d198dee33ab55e --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/differential_training/Qwen-Image-LoRA.sh @@ -0,0 +1,42 @@ +# This script is provided as an example only. +# Please manually replace the two datasets: +# the first training dataset should contain content you do not want to generate, +# and the second training dataset should contain content you do want to generate. + +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-LoRA-deterministic" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-LoRA-differencial" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters \ + --preset_lora_path "./models/train/Qwen-Image-LoRA-deterministic/epoch-4.safetensors" \ + --preset_lora_model "dit" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/fp8_training/Qwen-Image-LoRA.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/fp8_training/Qwen-Image-LoRA.sh new file mode 100644 index 0000000000000000000000000000000000000000..c45c4e48d5b71298c9b0db3fa2553622c3465cbc --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/fp8_training/Qwen-Image-LoRA.sh @@ -0,0 +1,19 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image_lora_fp8" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters \ + --fp8_models "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/fp8_training/validate.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/fp8_training/validate.py new file mode 100644 index 0000000000000000000000000000000000000000..60783e5e91e8a2854086390c92acd647b728ea0b --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/fp8_training/validate.py @@ -0,0 +1,18 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image_lora_fp8/epoch-4.safetensors") +prompt = "a dog" +image = pipe(prompt, seed=0) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/low_vram_training/Qwen-Image-LoRA.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/low_vram_training/Qwen-Image-LoRA.sh new file mode 100644 index 0000000000000000000000000000000000000000..9aa16f967c8500181223ef606b5dd93291cfaa14 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/low_vram_training/Qwen-Image-LoRA.sh @@ -0,0 +1,37 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 1 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image_lora-splited-cache" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --task "sft:data_process" \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters + +accelerate launch --config_file examples/qwen_image/model_training/special/low_vram_training/deepspeed_zero3_cpuoffload.yaml examples/qwen_image/model_training/train.py \ + --dataset_base_path "./models/train/Qwen-Image_lora-splited-cache" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --task "sft:train" \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters \ + --initialize_model_on_cpu diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/low_vram_training/deepspeed_zero3_cpuoffload.yaml b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/low_vram_training/deepspeed_zero3_cpuoffload.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9824b905b4fef8388bbe579966cbff457b372a89 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/low_vram_training/deepspeed_zero3_cpuoffload.yaml @@ -0,0 +1,18 @@ +compute_environment: LOCAL_MACHINE +debug: true +deepspeed_config: + deepspeed_config_file: examples/qwen_image/model_training/special/low_vram_training/ds_z3_cpuoffload.json + zero3_init_flag: true +distributed_type: DEEPSPEED +downcast_bf16: 'no' +enable_cpu_affinity: false +machine_rank: 0 +main_training_function: main +num_machines: 1 +num_processes: 1 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/low_vram_training/ds_z3_cpuoffload.json b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/low_vram_training/ds_z3_cpuoffload.json new file mode 100644 index 0000000000000000000000000000000000000000..c81f941a4c534d84666d734ad0ec69472dc00e60 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/low_vram_training/ds_z3_cpuoffload.json @@ -0,0 +1,43 @@ +{ + "fp16": { + "enabled": "auto", + "loss_scale": 0, + "loss_scale_window": 1000, + "initial_scale_power": 16, + "hysteresis": 2, + "min_loss_scale": 1 + }, + "bf16": { + "enabled": "auto" + }, + "zero_optimization": { + "stage": 3, + "offload_optimizer": { + "device": "cpu", + "pin_memory": true + }, + "offload_param": { + "device": "cpu", + "pin_memory": true + }, + "overlap_comm": false, + "contiguous_gradients": true, + "sub_group_size": 1e9, + "reduce_bucket_size": 5e7, + "stage3_prefetch_bucket_size": 5e7, + "stage3_param_persistence_threshold": 1e5, + "stage3_max_live_parameters": 1e8, + "stage3_max_reuse_distance": 1e8, + "stage3_gather_16bit_weights_on_model_save": true + }, + "activation_checkpointing": { + "partition_activations": false, + "cpu_checkpointing": false, + "contiguous_memory_optimization": false + }, + "gradient_accumulation_steps": "auto", + "gradient_clipping": "auto", + "train_batch_size": "auto", + "train_micro_batch_size_per_gpu": "auto", + "wall_clock_breakdown": false +} \ No newline at end of file diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/npu_training/Qwen-Image-Edit-2509-LoRA-NPU.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/npu_training/Qwen-Image-Edit-2509-LoRA-NPU.sh new file mode 100644 index 0000000000000000000000000000000000000000..97ae664a4288f10132b557ad05e30354247eddc7 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/npu_training/Qwen-Image-Edit-2509-LoRA-NPU.sh @@ -0,0 +1,40 @@ +# Due to memory limitations, split training is required to train the model on NPU +export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True +export CPU_AFFINITY_CONF=1 + +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Edit-2509/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Edit-2509 \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Edit-2509/metadata.json \ + --max_pixels 1048576 \ + --dataset_repeat 1 \ + --model_id_with_origin_paths "Qwen/Qwen-Image-Edit-2509:text_encoder/model*.safetensors,Qwen/Qwen-Image-Edit-2509:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Edit-2509-LoRA-splited-cache" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters \ + --task "sft:data_process" + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path "./models/train/Qwen-Image-Edit-2509-LoRA-splited-cache" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image-Edit-2509:transformer/diffusion_pytorch_model*.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Edit-2509-LoRA-splited" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters \ + --task "sft:train" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/npu_training/Qwen-Image-Edit-2509-NPU.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/npu_training/Qwen-Image-Edit-2509-NPU.sh new file mode 100644 index 0000000000000000000000000000000000000000..ce77b0d0f7840149a40172c9bb498aada4a15dc1 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/npu_training/Qwen-Image-Edit-2509-NPU.sh @@ -0,0 +1,22 @@ +# This script was tested using zero3 and on 8*910B(NPU) +export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True +export CPU_AFFINITY_CONF=1 + +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image-Edit-2509/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero3.yaml examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Edit-2509 \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image-Edit-2509/metadata.json \ + --data_file_keys "image,edit_image" \ + --extra_inputs "edit_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image-Edit-2509:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Edit-2509_full" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --find_unused_parameters \ + --initialize_model_on_cpu diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/npu_training/Qwen-Image-LoRA-NPU.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/npu_training/Qwen-Image-LoRA-NPU.sh new file mode 100644 index 0000000000000000000000000000000000000000..c2d83662db800f8f0aeefdbeb12ea485731d1ed2 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/npu_training/Qwen-Image-LoRA-NPU.sh @@ -0,0 +1,40 @@ +# Due to memory limitations, split training is required to train the model on NPU +export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True +export CPU_AFFINITY_CONF=1 + +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 1 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-LoRA-splited-cache" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters \ + --task "sft:data_process" + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path "./models/train/Qwen-Image-LoRA-splited-cache" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-LoRA-splited" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters \ + --task "sft:train" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/simple/train.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/simple/train.py new file mode 100644 index 0000000000000000000000000000000000000000..b4e6a112485474b9a3ff3885a4c1ba89fa746a37 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/simple/train.py @@ -0,0 +1,76 @@ +import torch, accelerate +from diffsynth.core import UnifiedDataset +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth.diffusion import * + +class QwenImageTrainingModule(DiffusionTrainingModule): + def __init__(self, device): + super().__init__() + # Load the pipeline + self.pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device=device, + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), + ) + # Switch to training mode + self.switch_pipe_to_training_mode( + self.pipe, + lora_base_model="dit", + lora_target_modules="to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj", + lora_rank=32, + ) + + def forward(self, data): + # Preprocess + inputs_posi = {"prompt": data["prompt"]} + inputs_nega = {"negative_prompt": ""} + inputs_shared = { + # Assume you are using this pipeline for inference, + # please fill in the input parameters. + "input_image": data["image"], + "height": data["image"].size[1], + "width": data["image"].size[0], + # Please do not modify the following parameters + # unless you clearly know what this will cause. + "cfg_scale": 1, + "rand_device": self.pipe.device, + "use_gradient_checkpointing": True, + "use_gradient_checkpointing_offload": False, + } + for unit in self.pipe.units: + inputs_shared, inputs_posi, inputs_nega = self.pipe.unit_runner(unit, self.pipe, inputs_shared, inputs_posi, inputs_nega) + # Loss + loss = FlowMatchSFTLoss(self.pipe, **inputs_shared, **inputs_posi) + return loss + +if __name__ == "__main__": + accelerator = accelerate.Accelerator( + kwargs_handlers=[accelerate.DistributedDataParallelKwargs(find_unused_parameters=True)], + ) + dataset = UnifiedDataset( + base_path="data/example_image_dataset", + metadata_path="data/example_image_dataset/metadata.csv", + repeat=50, + data_file_keys="image", + main_data_operator=UnifiedDataset.default_image_operator( + base_path="data/example_image_dataset", + height=512, + width=512, + height_division_factor=16, + width_division_factor=16, + ) + ) + model = QwenImageTrainingModule(accelerator.device) + model_logger = ModelLogger( + output_path="models/toy_model", + remove_prefix_in_ckpt="pipe.dit.", + ) + launch_training_task( + accelerator, dataset, model, model_logger, + learning_rate=1e-5, num_epochs=1, + ) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/split_training/Qwen-Image-LoRA.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/split_training/Qwen-Image-LoRA.sh new file mode 100644 index 0000000000000000000000000000000000000000..e08b2bc2c09ab1f07ff9d4b05fc44408eb4546e3 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/split_training/Qwen-Image-LoRA.sh @@ -0,0 +1,36 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "qwen_image/Qwen-Image/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/qwen_image/Qwen-Image \ + --dataset_metadata_path data/diffsynth_example_dataset/qwen_image/Qwen-Image/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 1 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-LoRA-splited-cache" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters \ + --task "sft:data_process" + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path "./models/train/Qwen-Image-LoRA-splited-cache" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-LoRA-splited" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters \ + --task "sft:train" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/split_training/validate.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/split_training/validate.py new file mode 100644 index 0000000000000000000000000000000000000000..a2f9e07a9a618bbe249e63532ee58d2f74f86393 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/special/split_training/validate.py @@ -0,0 +1,18 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-LoRA-splited/epoch-4.safetensors") +prompt = "a dog" +image = pipe(prompt, seed=0) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/train.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/train.py new file mode 100644 index 0000000000000000000000000000000000000000..63384ad37fbd44f131d61a056ace03c27548f1cd --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/train.py @@ -0,0 +1,183 @@ +import torch, os, argparse, accelerate +from diffsynth.core import UnifiedDataset +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth.diffusion import * +from diffsynth.core.data.operators import * +os.environ["TOKENIZERS_PARALLELISM"] = "false" + + +class QwenImageTrainingModule(DiffusionTrainingModule): + def __init__( + self, + model_paths=None, model_id_with_origin_paths=None, + tokenizer_path=None, processor_path=None, + trainable_models=None, + lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None, + preset_lora_path=None, preset_lora_model=None, + use_gradient_checkpointing=True, + use_gradient_checkpointing_offload=False, + extra_inputs=None, + fp8_models=None, + offload_models=None, + resume_from_checkpoint=None, remove_prefix_in_ckpt=None, + device="cpu", + task="sft", + zero_cond_t=False, + ): + super().__init__() + # Load models + model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, fp8_models=fp8_models, offload_models=offload_models, device=device) + tokenizer_config = ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/") if tokenizer_path is None else ModelConfig(tokenizer_path) + processor_config = ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/") if processor_path is None else ModelConfig(processor_path) + self.pipe = QwenImagePipeline.from_pretrained(torch_dtype=torch.bfloat16, device=device, model_configs=model_configs, tokenizer_config=tokenizer_config, processor_config=processor_config) + self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model) + self.resume_from_checkpoint(resume_from_checkpoint, remove_prefix_in_ckpt) + + # Training mode + self.switch_pipe_to_training_mode( + self.pipe, trainable_models, + lora_base_model, lora_target_modules, lora_rank, lora_checkpoint, + preset_lora_path, preset_lora_model, + task=task, + ) + + # Other configs + self.use_gradient_checkpointing = use_gradient_checkpointing + self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload + self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else [] + self.fp8_models = fp8_models + self.task = task + self.zero_cond_t = zero_cond_t + self.task_to_loss = { + "sft:data_process": lambda pipe, *args: args, + "direct_distill:data_process": lambda pipe, *args: args, + "sft": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi), + "sft:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi), + "direct_distill": lambda pipe, inputs_shared, inputs_posi, inputs_nega: DirectDistillLoss(pipe, **inputs_shared, **inputs_posi), + "direct_distill:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: DirectDistillLoss(pipe, **inputs_shared, **inputs_posi), + } + + def get_pipeline_inputs(self, data): + inputs_posi = {"prompt": data["prompt"]} + inputs_nega = {"negative_prompt": ""} + inputs_shared = { + # Please do not modify the following parameters + # unless you clearly know what this will cause. + "cfg_scale": 1, + "rand_device": self.pipe.device, + "use_gradient_checkpointing": self.use_gradient_checkpointing, + "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload, + "edit_image_auto_resize": True, + "zero_cond_t": self.zero_cond_t, + } + # Assume you are using this pipeline for inference, + # please fill in the input parameters. + if isinstance(data["image"], list): + inputs_shared.update({ + "input_image": data["image"], + "height": data["image"][0].size[1], + "width": data["image"][0].size[0], + }) + else: + inputs_shared.update({ + "input_image": data["image"], + "height": data["image"].size[1], + "width": data["image"].size[0], + }) + inputs_shared = self.parse_extra_inputs(data, self.extra_inputs, inputs_shared) + return inputs_shared, inputs_posi, inputs_nega + + def forward(self, data, inputs=None): + if inputs is None: inputs = self.get_pipeline_inputs(data) + inputs = self.transfer_data_to_device(inputs, self.pipe.device, self.pipe.torch_dtype) + for unit in self.pipe.units: + inputs = self.pipe.unit_runner(unit, self.pipe, *inputs) + loss = self.task_to_loss[self.task](self.pipe, *inputs) + return loss + + +def qwen_image_parser(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser = add_general_config(parser) + parser = add_image_size_config(parser) + parser.add_argument("--tokenizer_path", type=str, default=None, help="Path to tokenizer.") + parser.add_argument("--processor_path", type=str, default=None, help="Path to the processor. If provided, the processor will be used for image editing.") + parser.add_argument("--zero_cond_t", default=False, action="store_true", help="A special parameter introduced by Qwen-Image-Edit-2511. Please enable it for this model.") + parser.add_argument("--initialize_model_on_cpu", default=False, action="store_true", help="Whether to initialize models on CPU.") + return parser + + +if __name__ == "__main__": + parser = qwen_image_parser() + args = parser.parse_args() + accelerator = accelerate.Accelerator( + gradient_accumulation_steps=args.gradient_accumulation_steps, + kwargs_handlers=[accelerate.DistributedDataParallelKwargs(find_unused_parameters=args.find_unused_parameters)], + ) + dataset = UnifiedDataset( + base_path=args.dataset_base_path, + metadata_path=args.dataset_metadata_path, + repeat=args.dataset_repeat, + data_file_keys=args.data_file_keys.split(","), + main_data_operator=UnifiedDataset.default_image_operator( + base_path=args.dataset_base_path, + max_pixels=args.max_pixels, + height=args.height, + width=args.width, + height_division_factor=16, + width_division_factor=16, + ), + special_operator_map={ + # Qwen-Image-Layered + "layer_input_image": ToAbsolutePath(args.dataset_base_path) >> LoadImage(convert_RGB=False, convert_RGBA=True) >> ImageCropAndResize(args.height, args.width, args.max_pixels, 16, 16), + "image": RouteByType(operator_map=[ + (str, ToAbsolutePath(args.dataset_base_path) >> LoadImage() >> ImageCropAndResize(args.height, args.width, args.max_pixels, 16, 16)), + (list, SequencialProcess(ToAbsolutePath(args.dataset_base_path) >> LoadImage(convert_RGB=False, convert_RGBA=True) >> ImageCropAndResize(args.height, args.width, args.max_pixels, 16, 16))), + ]), + "context_image": RouteByType(operator_map=[ + (str, ToAbsolutePath(args.dataset_base_path) >> LoadImage() >> ImageCropAndResize(args.height, args.width, args.max_pixels, 16, 16)), + (None, lambda x: None), + ]) + } + ) + model = QwenImageTrainingModule( + model_paths=args.model_paths, + model_id_with_origin_paths=args.model_id_with_origin_paths, + tokenizer_path=args.tokenizer_path, + processor_path=args.processor_path, + trainable_models=args.trainable_models, + lora_base_model=args.lora_base_model, + lora_target_modules=args.lora_target_modules, + lora_rank=args.lora_rank, + lora_checkpoint=args.lora_checkpoint, + preset_lora_path=args.preset_lora_path, + preset_lora_model=args.preset_lora_model, + use_gradient_checkpointing=args.use_gradient_checkpointing, + use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload, + extra_inputs=args.extra_inputs, + fp8_models=args.fp8_models, + offload_models=args.offload_models, + resume_from_checkpoint=args.resume_from_checkpoint, + remove_prefix_in_ckpt=args.remove_prefix_in_ckpt, + task=args.task, + device="cpu" if (args.initialize_model_on_cpu or args.enable_model_cpu_offload) else accelerator.device, + zero_cond_t=args.zero_cond_t, + ) + model_logger = ModelLogger( + args.output_path, + remove_prefix_in_ckpt=args.remove_prefix_in_ckpt, + enable_tensorboard_log=args.enable_tensorboard_log, + enable_swanlab_log=args.enable_swanlab_log, + swanlab_project=args.swanlab_project, + enable_wandb_log=args.enable_wandb_log, + wandb_project=args.wandb_project, + ) + launcher_map = { + "sft:data_process": launch_data_process_task, + "direct_distill:data_process": launch_data_process_task, + "sft": launch_training_task, + "sft:train": launch_training_task, + "direct_distill": launch_training_task, + "direct_distill:train": launch_training_task, + } + launcher_map[args.task](accelerator, dataset, model, model_logger, args=args) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/FireRed-Image-Edit-1.0.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/FireRed-Image-Edit-1.0.py new file mode 100644 index 0000000000000000000000000000000000000000..3c0d07b4c55ab12bf639050bc8908ddc917280ab --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/FireRed-Image-Edit-1.0.py @@ -0,0 +1,26 @@ +import torch +from PIL import Image +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth import load_state_dict + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="FireRedTeam/FireRed-Image-Edit-1.0", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=None, + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +state_dict = load_state_dict("models/train/FireRed-Image-Edit-1.0_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +prompt = "Change the color of the dress in Figure 1 to the color shown in Figure 2." +images = [ + Image.open("data/example_image_dataset/edit/image1.jpg").resize((1024, 1024)), + Image.open("data/example_image_dataset/edit/image_color.jpg").resize((1024, 1024)), +] +image = pipe(prompt, edit_image=images, seed=123, num_inference_steps=40, height=1024, width=1024) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/FireRed-Image-Edit-1.1.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/FireRed-Image-Edit-1.1.py new file mode 100644 index 0000000000000000000000000000000000000000..02801dc98090ab8e5f9953ead93c3b3bbf0395e2 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/FireRed-Image-Edit-1.1.py @@ -0,0 +1,26 @@ +import torch +from PIL import Image +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth import load_state_dict + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="FireRedTeam/FireRed-Image-Edit-1.1", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=None, + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +state_dict = load_state_dict("models/train/FireRed-Image-Edit-1.1_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +prompt = "Change the color of the dress in Figure 1 to the color shown in Figure 2." +images = [ + Image.open("data/example_image_dataset/edit/image1.jpg").resize((1024, 1024)), + Image.open("data/example_image_dataset/edit/image_color.jpg").resize((1024, 1024)), +] +image = pipe(prompt, edit_image=images, seed=123, num_inference_steps=40, height=1024, width=1024) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-2512.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-2512.py new file mode 100644 index 0000000000000000000000000000000000000000..9dea6b72da61ae16c23ef4d94539903c9940c970 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-2512.py @@ -0,0 +1,20 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth import load_state_dict +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-2512", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +state_dict = load_state_dict("models/train/Qwen-Image-2512_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +prompt = "a dog" +image = pipe(prompt, seed=0) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py new file mode 100644 index 0000000000000000000000000000000000000000..6ae4d5bb72444f8d7550dc1856a2a0275b224ed9 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py @@ -0,0 +1,31 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput +from PIL import Image +import torch +from modelscope import dataset_snapshot_download + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ModelConfig(path="models/train/Qwen-Image-Blockwise-ControlNet-Canny_full/epoch-1.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="canny/image_1.jpg" +) +controlnet_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328)) + +prompt = "一只小狗，毛发光洁柔顺，眼神灵动，背景是樱花纷飞的春日庭院，唯美温馨。" +image = pipe( + prompt, seed=0, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)] +) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py new file mode 100644 index 0000000000000000000000000000000000000000..18b597e1fa7508127c431aa4a466c86392a83044 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py @@ -0,0 +1,31 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput +from PIL import Image +import torch +from modelscope import dataset_snapshot_download + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ModelConfig(path="models/train/Qwen-Image-Blockwise-ControlNet-Depth_full/epoch-1.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="depth/image_1.jpg" +) +controlnet_image = Image.open("data/example_image_dataset/depth/image_1.jpg").resize((1328, 1328)) + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe( + prompt, seed=0, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)] +) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py new file mode 100644 index 0000000000000000000000000000000000000000..15a15b4df8bd9608f8ce6782a309d37699b923d1 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py @@ -0,0 +1,32 @@ +import torch +from PIL import Image +from modelscope import dataset_snapshot_download +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ModelConfig(path="models/train/Qwen-Image-Blockwise-ControlNet-Inpaint_full/epoch-1.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="inpaint/*.jpg" +) +prompt = "a cat with sunglasses" +controlnet_image = Image.open("./data/example_image_dataset/inpaint/image_1.jpg").convert("RGB").resize((1024, 1024)) +inpaint_mask = Image.open("./data/example_image_dataset/inpaint/mask.jpg").convert("RGB").resize((1024, 1024)) +image = pipe( + prompt, seed=0, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image, inpaint_mask=inpaint_mask)], + height=1024, width=1024, + num_inference_steps=40, +) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py new file mode 100644 index 0000000000000000000000000000000000000000..07389c524c2e142679d866257785030eb15795a2 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py @@ -0,0 +1,20 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth import load_state_dict +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Distill-Full", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +state_dict = load_state_dict("models/train/Qwen-Image-Distill-Full_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +prompt = "a dog" +image = pipe(prompt, seed=0, num_inference_steps=15, cfg_scale=1) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py new file mode 100644 index 0000000000000000000000000000000000000000..9295904cacc2c00bfd0eeb801f2f10343fc89f45 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py @@ -0,0 +1,26 @@ +import torch +from PIL import Image +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth import load_state_dict + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit-2509", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=None, + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +state_dict = load_state_dict("models/train/Qwen-Image-Edit-2509_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +prompt = "Change the color of the dress in Figure 1 to the color shown in Figure 2." +images = [ + Image.open("data/example_image_dataset/edit/image1.jpg").resize((1024, 1024)), + Image.open("data/example_image_dataset/edit/image_color.jpg").resize((1024, 1024)), +] +image = pipe(prompt, edit_image=images, seed=123, num_inference_steps=40, height=1024, width=1024) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2511.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2511.py new file mode 100644 index 0000000000000000000000000000000000000000..c94dadc541ae902aaacf4c483c6cd551c68eadf0 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2511.py @@ -0,0 +1,26 @@ +import torch +from PIL import Image +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth import load_state_dict + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit-2511", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=None, + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +state_dict = load_state_dict("models/train/Qwen-Image-Edit-2511_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +prompt = "Change the color of the dress in Figure 1 to the color shown in Figure 2." +images = [ + Image.open("data/example_image_dataset/edit/image1.jpg").resize((1024, 1024)), + Image.open("data/example_image_dataset/edit/image_color.jpg").resize((1024, 1024)), +] +image = pipe(prompt, edit_image=images, seed=123, num_inference_steps=40, height=1024, width=1024, zero_cond_t=True) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py new file mode 100644 index 0000000000000000000000000000000000000000..c08b4850c4d20925a7d008853d11b213ee3d69b6 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py @@ -0,0 +1,23 @@ +import torch +from PIL import Image +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth import load_state_dict + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=None, + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +state_dict = load_state_dict("models/train/Qwen-Image-Edit_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +prompt = "将裙子改为粉色" +image = Image.open("data/example_image_dataset/edit/image1.jpg").resize((1024, 1024)) +image = pipe(prompt, edit_image=image, seed=0, num_inference_steps=40, height=1024, width=1024) +image.save(f"image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..961904f93bee36e2225585c7a0e257f704ba064e --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered-Control.py @@ -0,0 +1,26 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth import load_state_dict +from PIL import Image +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Layered-Control", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +state_dict = load_state_dict("models/train/Qwen-Image-Layered-Control_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +prompt = "Text 'HELLO' and 'Have a great day'" +input_image = Image.open("data/example_image_dataset/layer/image.png").convert("RGBA").resize((864, 480)) +images = pipe( + prompt, seed=0, + height=480, width=864, + layer_input_image=input_image, layer_num=0, +) +images[0].save("image.png") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered.py new file mode 100644 index 0000000000000000000000000000000000000000..dc845498b6e54209391cfada99bf64979765e71d --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered.py @@ -0,0 +1,28 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth import load_state_dict +from PIL import Image +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +state_dict = load_state_dict("models/train/Qwen-Image-Layered_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +prompt = "a poster" +input_image = Image.open("data/example_image_dataset/layer/image.png").convert("RGBA").resize((864, 480)) +images = pipe( + prompt, seed=0, + height=480, width=864, + layer_input_image=input_image, layer_num=3, +) +for i, image in enumerate(images): + if i == 0: continue # The first image is the input image. + image.save(f"image_{i}.png") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image.py new file mode 100644 index 0000000000000000000000000000000000000000..872321825eb4028fe18d5b00c8ee815bc6356e75 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_full/Qwen-Image.py @@ -0,0 +1,20 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth import load_state_dict +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +state_dict = load_state_dict("models/train/Qwen-Image_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +prompt = "a dog" +image = pipe(prompt, seed=0) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/FireRed-Image-Edit-1.0.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/FireRed-Image-Edit-1.0.py new file mode 100644 index 0000000000000000000000000000000000000000..b5b0046e141eb0c0c39020ca5c66ce76a06466dd --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/FireRed-Image-Edit-1.0.py @@ -0,0 +1,24 @@ +import torch +from PIL import Image +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="FireRedTeam/FireRed-Image-Edit-1.0", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=None, + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +pipe.load_lora(pipe.dit, "models/train/FireRed-Image-Edit-1.0_lora/epoch-4.safetensors") + +prompt = "Change the color of the dress in Figure 1 to the color shown in Figure 2." +images = [ + Image.open("data/example_image_dataset/edit/image1.jpg").resize((1024, 1024)), + Image.open("data/example_image_dataset/edit/image_color.jpg").resize((1024, 1024)), +] +image = pipe(prompt, edit_image=images, seed=123, num_inference_steps=40, height=1024, width=1024) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/FireRed-Image-Edit-1.1.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/FireRed-Image-Edit-1.1.py new file mode 100644 index 0000000000000000000000000000000000000000..234105ba3fada9af10245237ff7664e45e764499 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/FireRed-Image-Edit-1.1.py @@ -0,0 +1,24 @@ +import torch +from PIL import Image +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="FireRedTeam/FireRed-Image-Edit-1.1", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=None, + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +pipe.load_lora(pipe.dit, "models/train/FireRed-Image-Edit-1.1_lora/epoch-4.safetensors") + +prompt = "Change the color of the dress in Figure 1 to the color shown in Figure 2." +images = [ + Image.open("data/example_image_dataset/edit/image1.jpg").resize((1024, 1024)), + Image.open("data/example_image_dataset/edit/image_color.jpg").resize((1024, 1024)), +] +image = pipe(prompt, edit_image=images, seed=123, num_inference_steps=40, height=1024, width=1024) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-2512.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-2512.py new file mode 100644 index 0000000000000000000000000000000000000000..ee829d884db076ab711ceb310b165f9285647db0 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-2512.py @@ -0,0 +1,18 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-2512", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-2512_lora/epoch-4.safetensors") +prompt = "a dog" +image = pipe(prompt, seed=0) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py new file mode 100644 index 0000000000000000000000000000000000000000..4a54b5ee07316dcca50e439ec1083d392305ce18 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py @@ -0,0 +1,32 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput +from PIL import Image +import torch +from modelscope import dataset_snapshot_download + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny", origin_file_pattern="model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Blockwise-ControlNet-Canny_lora/epoch-4.safetensors") + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="canny/image_1.jpg" +) +controlnet_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328)) + +prompt = "一只小狗，毛发光洁柔顺，眼神灵动，背景是樱花纷飞的春日庭院，唯美温馨。" +image = pipe( + prompt, seed=0, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)] +) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py new file mode 100644 index 0000000000000000000000000000000000000000..626654559d1970697a2b9a12af3722581956bfbb --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py @@ -0,0 +1,33 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput +from PIL import Image +import torch +from modelscope import dataset_snapshot_download + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth", origin_file_pattern="model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Blockwise-ControlNet-Depth_lora/epoch-4.safetensors") + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="depth/image_1.jpg" +) + +controlnet_image = Image.open("data/example_image_dataset/depth/image_1.jpg").resize((1328, 1328)) + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe( + prompt, seed=0, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)] +) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py new file mode 100644 index 0000000000000000000000000000000000000000..60bd9f2bbbefd95ab0efe1c7e07aaeb96f4a560d --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py @@ -0,0 +1,34 @@ +import torch +from PIL import Image +from modelscope import dataset_snapshot_download +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint", origin_file_pattern="model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Blockwise-ControlNet-Inpaint_lora/epoch-4.safetensors") + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="inpaint/*.jpg" +) +prompt = "a cat with sunglasses" +controlnet_image = Image.open("./data/example_image_dataset/inpaint/image_1.jpg").convert("RGB").resize((1024, 1024)) +inpaint_mask = Image.open("./data/example_image_dataset/inpaint/mask.jpg").convert("RGB").resize((1024, 1024)) +image = pipe( + prompt, seed=0, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image, inpaint_mask=inpaint_mask)], + height=1024, width=1024, + num_inference_steps=40, +) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py new file mode 100644 index 0000000000000000000000000000000000000000..7f644aa805d47bdc04711b28d5ea0d3d3eafc36e --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py @@ -0,0 +1,18 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Distill-Full", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Distill-Full_lora/epoch-4.safetensors") +prompt = "a dog" +image = pipe(prompt, seed=0, num_inference_steps=15, cfg_scale=1) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py new file mode 100644 index 0000000000000000000000000000000000000000..d56b9af7f4c6e8bf6e83845c3c2511a2a9374810 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py @@ -0,0 +1,23 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Distill-LoRA_lora/epoch-4.safetensors") +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe( + prompt, + seed=0, + num_inference_steps=4, + cfg_scale=1, +) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py new file mode 100644 index 0000000000000000000000000000000000000000..e701b0749809f08db27dd52c37dc93445d0f4995 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py @@ -0,0 +1,24 @@ +import torch +from PIL import Image +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit-2509", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=None, + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Edit-2509_lora/epoch-4.safetensors") + +prompt = "Change the color of the dress in Figure 1 to the color shown in Figure 2." +images = [ + Image.open("data/example_image_dataset/edit/image1.jpg").resize((1024, 1024)), + Image.open("data/example_image_dataset/edit/image_color.jpg").resize((1024, 1024)), +] +image = pipe(prompt, edit_image=images, seed=123, num_inference_steps=40, height=1024, width=1024) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2511.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2511.py new file mode 100644 index 0000000000000000000000000000000000000000..0cd0fc7bdbd4704a4957024b839bfe2ff5717d17 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2511.py @@ -0,0 +1,24 @@ +import torch +from PIL import Image +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit-2511", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=None, + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Edit-2511_lora/epoch-4.safetensors") + +prompt = "Change the color of the dress in Figure 1 to the color shown in Figure 2." +images = [ + Image.open("data/example_image_dataset/edit/image1.jpg").resize((1024, 1024)), + Image.open("data/example_image_dataset/edit/image_color.jpg").resize((1024, 1024)), +] +image = pipe(prompt, edit_image=images, seed=123, num_inference_steps=40, height=1024, width=1024, zero_cond_t=True) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py new file mode 100644 index 0000000000000000000000000000000000000000..2576be339df5fba385c37567af0902842c306289 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py @@ -0,0 +1,21 @@ +import torch +from PIL import Image +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=None, + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Edit_lora/epoch-4.safetensors") + +prompt = "将裙子改为粉色" +image = Image.open("data/example_image_dataset/edit/image1.jpg").resize((1024, 1024)) +image = pipe(prompt, edit_image=image, seed=0, num_inference_steps=40, height=1024, width=1024) +image.save(f"image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py new file mode 100644 index 0000000000000000000000000000000000000000..81f697c4073b04d242761e90c5b6159a3a4e3505 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py @@ -0,0 +1,29 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch +from PIL import Image + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-EliGen-Poster_lora/epoch-4.safetensors") + + +entity_prompts = ["A beautiful girl", "sign 'Entity Control'", "shorts", "shirt"] +global_prompt = "A beautiful girl wearing shirt and shorts in the street, holding a sign 'Entity Control'" +masks = [Image.open(f"data/example_image_dataset/eligen/{i}.png").convert('RGB') for i in range(len(entity_prompts))] + +image = pipe(global_prompt, + seed=0, + height=1024, + width=1024, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks) +image.save("Qwen-Image-EliGen-Poster.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py new file mode 100644 index 0000000000000000000000000000000000000000..cd7904e9d844232c599b7534bc58398e1820702b --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py @@ -0,0 +1,29 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch +from PIL import Image + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-EliGen_lora/epoch-4.safetensors") + + +entity_prompts = ["A beautiful girl", "sign 'Entity Control'", "shorts", "shirt"] +global_prompt = "A beautiful girl wearing shirt and shorts in the street, holding a sign 'Entity Control'" +masks = [Image.open(f"data/example_image_dataset/eligen/{i}.png").convert('RGB') for i in range(len(entity_prompts))] + +image = pipe(global_prompt, + seed=0, + height=1024, + width=1024, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks) +image.save("Qwen-Image_EliGen.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py new file mode 100644 index 0000000000000000000000000000000000000000..83a93a3f6bf41529c9350052e03f8f95050c8eb5 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py @@ -0,0 +1,19 @@ +from PIL import Image +import torch +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-In-Context-Control-Union_lora/epoch-4.safetensors") +image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1024, 1024)) +prompt = "Context_Control. a dog" +image = pipe(prompt=prompt, seed=0, context_image=image, height=1024, width=1024) +image.save("image_context.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered-Control-V2.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered-Control-V2.py new file mode 100644 index 0000000000000000000000000000000000000000..3aa7ac4e44ec333b971604f9222c5d64fcc35854 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered-Control-V2.py @@ -0,0 +1,37 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from modelscope import dataset_snapshot_download +from PIL import Image +import torch + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Layered-Control", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Layered-Control-V2_lora/epoch-4.safetensors") + +prompt = "Text 'APRIL'" +input_image = Image.open("data/example_image_dataset/layer_v2/image_1.png").convert("RGBA").resize((1024, 1024)) +image = pipe( + prompt, seed=0, + height=1024, width=1024, + layer_input_image=input_image, layer_num=0, + num_inference_steps=10, cfg_scale=4, +) +image[0].save("image_prompt.png") + +mask_image = Image.open("data/example_image_dataset/layer_v2/mask_2.png").convert("RGBA").resize((1024, 1024)) +input_image = Image.open("data/example_image_dataset/layer_v2/image_2.png").convert("RGBA").resize((1024, 1024)) +image = pipe( + prompt, seed=0, + height=1024, width=1024, + layer_input_image=input_image, layer_num=0, + context_image=mask_image, + num_inference_steps=10, cfg_scale=1.0, +) +image[0].save("image_mask.png") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..1a96e8b43e60584c3da3b6b871bd811f95480b5c --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered-Control.py @@ -0,0 +1,25 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth import load_state_dict +from PIL import Image +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Layered-Control", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Layered-Control_lora/epoch-4.safetensors") +prompt = "Text 'HELLO' and 'Have a great day'" +input_image = Image.open("data/example_image_dataset/layer/image.png").convert("RGBA").resize((864, 480)) +images = pipe( + prompt, seed=0, + height=480, width=864, + layer_input_image=input_image, layer_num=0, +) +images[0].save("image.png") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered.py new file mode 100644 index 0000000000000000000000000000000000000000..4ae4c8b6c06ba8177473ad637cdacb39abf925f3 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered.py @@ -0,0 +1,27 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth import load_state_dict +from PIL import Image +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Layered_lora/epoch-4.safetensors") +prompt = "a poster" +input_image = Image.open("data/example_image_dataset/layer/image.png").convert("RGBA").resize((864, 480)) +images = pipe( + prompt, seed=0, + height=480, width=864, + layer_input_image=input_image, layer_num=3, +) +for i, image in enumerate(images): + if i == 0: continue # The first image is the input image. + image.save(f"image_{i}.png") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image.py new file mode 100644 index 0000000000000000000000000000000000000000..16be2b4bda15c696c2b7bb4f3fd36176db67da0e --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/qwen_image/model_training/validate_lora/Qwen-Image.py @@ -0,0 +1,18 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image_lora/epoch-4.safetensors") +prompt = "a dog" +image = pipe(prompt, seed=0) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_inference/stable-diffusion-v1-5.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_inference/stable-diffusion-v1-5.py new file mode 100644 index 0000000000000000000000000000000000000000..2a07022876aa0747c7ce5760131c8a03cd2f72e6 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_inference/stable-diffusion-v1-5.py @@ -0,0 +1,25 @@ +import torch +from diffsynth.core import ModelConfig +from diffsynth.pipelines.stable_diffusion import StableDiffusionPipeline + +pipe = StableDiffusionPipeline.from_pretrained( + torch_dtype=torch.float32, + model_configs=[ + ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="unet/diffusion_pytorch_model.safetensors"), + ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="tokenizer/"), +) + +image = pipe( + prompt="a photo of an astronaut riding a horse on mars, high quality, detailed", + negative_prompt="blurry, low quality, deformed", + cfg_scale=7.5, + height=512, + width=512, + seed=42, + rand_device="cuda", + num_inference_steps=50, +) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_inference_low_vram/stable-diffusion-v1-5.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_inference_low_vram/stable-diffusion-v1-5.py new file mode 100644 index 0000000000000000000000000000000000000000..3d6fb5912430f9e98bcf7ca064a2a1396a831507 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_inference_low_vram/stable-diffusion-v1-5.py @@ -0,0 +1,36 @@ +import torch +from diffsynth.core import ModelConfig +from diffsynth.pipelines.stable_diffusion import StableDiffusionPipeline + +vram_config = { + "offload_dtype": torch.float32, + "offload_device": "cpu", + "onload_dtype": torch.float32, + "onload_device": "cpu", + "preparing_dtype": torch.float32, + "preparing_device": "cuda", + "computation_dtype": torch.float32, + "computation_device": "cuda", +} +pipe = StableDiffusionPipeline.from_pretrained( + torch_dtype=torch.float32, + model_configs=[ + ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="text_encoder/model.safetensors", **vram_config), + ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="unet/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="tokenizer/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) + +image = pipe( + prompt="a photo of an astronaut riding a horse on mars, high quality, detailed", + negative_prompt="blurry, low quality, deformed", + cfg_scale=7.5, + height=512, + width=512, + seed=42, + rand_device="cuda", + num_inference_steps=50, +) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_training/full/stable-diffusion-v1-5.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_training/full/stable-diffusion-v1-5.sh new file mode 100644 index 0000000000000000000000000000000000000000..0396dedd2c85ac7da3f1da6428ded0a724d563c0 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_training/full/stable-diffusion-v1-5.sh @@ -0,0 +1,15 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "stable_diffusion/stable-diffusion-v1-5/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/stable_diffusion/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/stable_diffusion/stable-diffusion-v1-5 \ + --dataset_metadata_path data/diffsynth_example_dataset/stable_diffusion/stable-diffusion-v1-5/metadata.csv \ + --height 512 \ + --width 512 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "AI-ModelScope/stable-diffusion-v1-5:text_encoder/model.safetensors,AI-ModelScope/stable-diffusion-v1-5:unet/diffusion_pytorch_model.safetensors,AI-ModelScope/stable-diffusion-v1-5:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --trainable_models "unet" \ + --remove_prefix_in_ckpt "pipe.unet." \ + --output_path "./models/train/stable-diffusion-v1-5_full" \ + --use_gradient_checkpointing diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_training/lora/stable-diffusion-v1-5.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_training/lora/stable-diffusion-v1-5.sh new file mode 100644 index 0000000000000000000000000000000000000000..4b0905b433b183e4053c91eed133213d946fc62a --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_training/lora/stable-diffusion-v1-5.sh @@ -0,0 +1,17 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "stable_diffusion/stable-diffusion-v1-5/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/stable_diffusion/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/stable_diffusion/stable-diffusion-v1-5 \ + --dataset_metadata_path data/diffsynth_example_dataset/stable_diffusion/stable-diffusion-v1-5/metadata.csv \ + --height 512 \ + --width 512 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "AI-ModelScope/stable-diffusion-v1-5:text_encoder/model.safetensors,AI-ModelScope/stable-diffusion-v1-5:unet/diffusion_pytorch_model.safetensors,AI-ModelScope/stable-diffusion-v1-5:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.unet." \ + --output_path "./models/train/stable-diffusion-v1-5_lora" \ + --lora_base_model "unet" \ + --lora_target_modules "" \ + --lora_rank 32 \ + --use_gradient_checkpointing diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_training/train.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_training/train.py new file mode 100644 index 0000000000000000000000000000000000000000..e6f5b00a17941dfca6dc6fda53918c52f9221851 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_training/train.py @@ -0,0 +1,151 @@ +import torch, os, argparse, accelerate +from diffsynth.core import UnifiedDataset +from diffsynth.pipelines.stable_diffusion import StableDiffusionPipeline, ModelConfig +from diffsynth.diffusion import * +os.environ["TOKENIZERS_PARALLELISM"] = "false" + + +class StableDiffusionTrainingModule(DiffusionTrainingModule): + def __init__( + self, + model_paths=None, model_id_with_origin_paths=None, + tokenizer_path=None, + trainable_models=None, + lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None, + preset_lora_path=None, preset_lora_model=None, + use_gradient_checkpointing=True, + use_gradient_checkpointing_offload=False, + extra_inputs=None, + fp8_models=None, + offload_models=None, + resume_from_checkpoint=None, remove_prefix_in_ckpt=None, + device="cpu", + task="sft", + ): + super().__init__() + # Load models + model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, fp8_models=fp8_models, offload_models=offload_models, device=device) + tokenizer_config = self.parse_path_or_model_id(tokenizer_path, ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="tokenizer/")) + self.pipe = StableDiffusionPipeline.from_pretrained(torch_dtype=torch.float32, device=device, model_configs=model_configs, tokenizer_config=tokenizer_config) + self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model) + self.resume_from_checkpoint(resume_from_checkpoint, remove_prefix_in_ckpt) + + # Training mode + self.switch_pipe_to_training_mode( + self.pipe, trainable_models, + lora_base_model, lora_target_modules, lora_rank, lora_checkpoint, + preset_lora_path, preset_lora_model, + task=task, + ) + + # Other configs + self.use_gradient_checkpointing = use_gradient_checkpointing + self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload + self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else [] + self.fp8_models = fp8_models + self.task = task + self.task_to_loss = { + "sft:data_process": lambda pipe, *args: args, + "direct_distill:data_process": lambda pipe, *args: args, + "sft": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi), + "sft:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi), + "direct_distill": lambda pipe, inputs_shared, inputs_posi, inputs_nega: DirectDistillLoss(pipe, **inputs_shared, **inputs_posi), + "direct_distill:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: DirectDistillLoss(pipe, **inputs_shared, **inputs_posi), + } + + def get_pipeline_inputs(self, data): + inputs_posi = {"prompt": data["prompt"]} + inputs_nega = {"negative_prompt": ""} + inputs_shared = { + # Assume you are using this pipeline for inference, + # please fill in the input parameters. + "input_image": data["image"], + "height": data["image"].size[1], + "width": data["image"].size[0], + # Please do not modify the following parameters + # unless you clearly know what this will cause. + "cfg_scale": 1, + "rand_device": self.pipe.device, + "use_gradient_checkpointing": self.use_gradient_checkpointing, + "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload, + } + inputs_shared = self.parse_extra_inputs(data, self.extra_inputs, inputs_shared) + return inputs_shared, inputs_posi, inputs_nega + + def forward(self, data, inputs=None): + if inputs is None: inputs = self.get_pipeline_inputs(data) + inputs = self.transfer_data_to_device(inputs, self.pipe.device, self.pipe.torch_dtype) + for unit in self.pipe.units: + inputs = self.pipe.unit_runner(unit, self.pipe, *inputs) + loss = self.task_to_loss[self.task](self.pipe, *inputs) + return loss + + +def parser(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser = add_general_config(parser) + parser = add_image_size_config(parser) + parser.add_argument("--tokenizer_path", type=str, default=None, help="Path to tokenizer.") + return parser + + +if __name__ == "__main__": + parser = parser() + args = parser.parse_args() + accelerator = accelerate.Accelerator( + gradient_accumulation_steps=args.gradient_accumulation_steps, + kwargs_handlers=[accelerate.DistributedDataParallelKwargs(find_unused_parameters=args.find_unused_parameters)], + ) + dataset = UnifiedDataset( + base_path=args.dataset_base_path, + metadata_path=args.dataset_metadata_path, + repeat=args.dataset_repeat, + data_file_keys=args.data_file_keys.split(","), + main_data_operator=UnifiedDataset.default_image_operator( + base_path=args.dataset_base_path, + max_pixels=args.max_pixels, + height=args.height, + width=args.width, + height_division_factor=32, + width_division_factor=32, + ) + ) + model = StableDiffusionTrainingModule( + model_paths=args.model_paths, + model_id_with_origin_paths=args.model_id_with_origin_paths, + tokenizer_path=args.tokenizer_path, + trainable_models=args.trainable_models, + lora_base_model=args.lora_base_model, + lora_target_modules=args.lora_target_modules, + lora_rank=args.lora_rank, + lora_checkpoint=args.lora_checkpoint, + preset_lora_path=args.preset_lora_path, + preset_lora_model=args.preset_lora_model, + use_gradient_checkpointing=args.use_gradient_checkpointing, + use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload, + extra_inputs=args.extra_inputs, + fp8_models=args.fp8_models, + offload_models=args.offload_models, + resume_from_checkpoint=args.resume_from_checkpoint, + remove_prefix_in_ckpt=args.remove_prefix_in_ckpt, + task=args.task, + device="cpu" if args.enable_model_cpu_offload else accelerator.device, + ) + model_logger = ModelLogger( + args.output_path, + remove_prefix_in_ckpt=args.remove_prefix_in_ckpt, + enable_tensorboard_log=args.enable_tensorboard_log, + enable_swanlab_log=args.enable_swanlab_log, + swanlab_project=args.swanlab_project, + enable_wandb_log=args.enable_wandb_log, + wandb_project=args.wandb_project, + ) + launcher_map = { + "sft:data_process": launch_data_process_task, + "direct_distill:data_process": launch_data_process_task, + "sft": launch_training_task, + "sft:train": launch_training_task, + "direct_distill": launch_training_task, + "direct_distill:train": launch_training_task, + } + launcher_map[args.task](accelerator, dataset, model, model_logger, args=args) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_training/validate_full/stable-diffusion-v1-5.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_training/validate_full/stable-diffusion-v1-5.py new file mode 100644 index 0000000000000000000000000000000000000000..1daca486facc5d5e656598d78de43933c2298cc8 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_training/validate_full/stable-diffusion-v1-5.py @@ -0,0 +1,27 @@ +from diffsynth.pipelines.stable_diffusion import StableDiffusionPipeline, ModelConfig +from diffsynth.core import load_state_dict +import torch + +pipe = StableDiffusionPipeline.from_pretrained( + torch_dtype=torch.float32, + model_configs=[ + ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="unet/diffusion_pytorch_model.safetensors"), + ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="tokenizer/"), +) +state_dict = load_state_dict("./models/train/stable-diffusion-v1-5_full/epoch-1.safetensors", torch_dtype=torch.float32) +pipe.unet.load_state_dict(state_dict) + +image = pipe( + prompt="a dog", + negative_prompt="blurry, low quality, deformed", + cfg_scale=7.5, + height=512, + width=512, + seed=42, + rand_device="cuda", + num_inference_steps=50, +) +image.save("image_stable-diffusion-v1-5_full.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_training/validate_lora/stable-diffusion-v1-5.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_training/validate_lora/stable-diffusion-v1-5.py new file mode 100644 index 0000000000000000000000000000000000000000..a08e1a0fb9b715a345d3b35e52076d0798f2ead2 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion/model_training/validate_lora/stable-diffusion-v1-5.py @@ -0,0 +1,26 @@ +import torch +from diffsynth.core import ModelConfig +from diffsynth.pipelines.stable_diffusion import StableDiffusionPipeline + +pipe = StableDiffusionPipeline.from_pretrained( + torch_dtype=torch.float32, + model_configs=[ + ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="unet/diffusion_pytorch_model.safetensors"), + ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.unet, "models/train/stable-diffusion-v1-5_lora/epoch-4.safetensors") + +image = pipe( + prompt="a dog", + negative_prompt="blurry, low quality, deformed", + cfg_scale=7.5, + height=512, + width=512, + seed=42, + rand_device="cuda", + num_inference_steps=50, +) +image.save("image_stable-diffusion-v1-5.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_inference/stable-diffusion-xl-base-1.0.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_inference/stable-diffusion-xl-base-1.0.py new file mode 100644 index 0000000000000000000000000000000000000000..6689b48994ab7a70336e0ad81ac22d3e8606e227 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_inference/stable-diffusion-xl-base-1.0.py @@ -0,0 +1,26 @@ +import torch +from diffsynth.core import ModelConfig +from diffsynth.pipelines.stable_diffusion_xl import StableDiffusionXLPipeline + +pipe = StableDiffusionXLPipeline.from_pretrained( + torch_dtype=torch.float32, + model_configs=[ + ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="text_encoder_2/model.safetensors"), + ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="unet/diffusion_pytorch_model.safetensors"), + ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="tokenizer/"), + tokenizer_2_config=ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="tokenizer_2/"), +) + +image = pipe( + prompt="a photo of an astronaut riding a horse on mars", + negative_prompt="", + cfg_scale=5.0, + height=1024, + width=1024, + seed=42, + num_inference_steps=50, +) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_inference_low_vram/stable-diffusion-xl-base-1.0.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_inference_low_vram/stable-diffusion-xl-base-1.0.py new file mode 100644 index 0000000000000000000000000000000000000000..49d16fa424983c615d71c99634263c262059bd94 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_inference_low_vram/stable-diffusion-xl-base-1.0.py @@ -0,0 +1,37 @@ +import torch +from diffsynth.core import ModelConfig +from diffsynth.pipelines.stable_diffusion_xl import StableDiffusionXLPipeline + +vram_config = { + "offload_dtype": torch.float32, + "offload_device": "cpu", + "onload_dtype": torch.float32, + "onload_device": "cpu", + "preparing_dtype": torch.float32, + "preparing_device": "cuda", + "computation_dtype": torch.float32, + "computation_device": "cuda", +} +pipe = StableDiffusionXLPipeline.from_pretrained( + torch_dtype=torch.float32, + model_configs=[ + ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="text_encoder/model.safetensors", **vram_config), + ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="text_encoder_2/model.safetensors", **vram_config), + ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="unet/diffusion_pytorch_model.safetensors", **vram_config), + ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="tokenizer/"), + tokenizer_2_config=ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="tokenizer_2/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5, +) + +image = pipe( + prompt="a photo of an astronaut riding a horse on mars", + negative_prompt="", + cfg_scale=5.0, + height=1024, + width=1024, + seed=42, + num_inference_steps=50, +) +image.save("image.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_training/full/stable-diffusion-xl-base-1.0.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_training/full/stable-diffusion-xl-base-1.0.sh new file mode 100644 index 0000000000000000000000000000000000000000..1cdb82d5ded0442b4eea270adcc05ab555284d6a --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_training/full/stable-diffusion-xl-base-1.0.sh @@ -0,0 +1,15 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "stable_diffusion_xl/stable-diffusion-xl-base-1.0/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/stable_diffusion_xl/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/stable_diffusion_xl/stable-diffusion-xl-base-1.0 \ + --dataset_metadata_path data/diffsynth_example_dataset/stable_diffusion_xl/stable-diffusion-xl-base-1.0/metadata.csv \ + --height 1024 \ + --width 1024 \ + --dataset_repeat 10 \ + --model_id_with_origin_paths "stabilityai/stable-diffusion-xl-base-1.0:text_encoder/model.safetensors,stabilityai/stable-diffusion-xl-base-1.0:text_encoder_2/model.safetensors,stabilityai/stable-diffusion-xl-base-1.0:unet/diffusion_pytorch_model.safetensors,stabilityai/stable-diffusion-xl-base-1.0:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --trainable_models "unet" \ + --remove_prefix_in_ckpt "pipe.unet." \ + --output_path "./models/train/stable-diffusion-xl-base-1.0_full" \ + --use_gradient_checkpointing diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_training/lora/stable-diffusion-xl-base-1.0.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_training/lora/stable-diffusion-xl-base-1.0.sh new file mode 100644 index 0000000000000000000000000000000000000000..d958e80fa170ee8c44c62575bda7f39b0fd90413 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_training/lora/stable-diffusion-xl-base-1.0.sh @@ -0,0 +1,18 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "stable_diffusion_xl/stable-diffusion-xl-base-1.0/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/stable_diffusion_xl/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/stable_diffusion_xl/stable-diffusion-xl-base-1.0 \ + --dataset_metadata_path data/diffsynth_example_dataset/stable_diffusion_xl/stable-diffusion-xl-base-1.0/metadata.csv \ + --height 1024 \ + --width 1024 \ + --dataset_repeat 10 \ + --model_id_with_origin_paths "stabilityai/stable-diffusion-xl-base-1.0:text_encoder/model.safetensors,stabilityai/stable-diffusion-xl-base-1.0:text_encoder_2/model.safetensors,stabilityai/stable-diffusion-xl-base-1.0:unet/diffusion_pytorch_model.safetensors,stabilityai/stable-diffusion-xl-base-1.0:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.unet." \ + --output_path "./models/train/stable-diffusion-xl-base-1.0_lora" \ + --lora_base_model "unet" \ + --lora_target_modules 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\ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --align_to_opensource_format diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_training/train.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_training/train.py new file mode 100644 index 0000000000000000000000000000000000000000..45b144528b57595b4519bd58696bfe2e80d60ed8 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_training/train.py @@ -0,0 +1,156 @@ +import torch, os, argparse, accelerate +from diffsynth.core import UnifiedDataset +from diffsynth.pipelines.stable_diffusion_xl import StableDiffusionXLPipeline, ModelConfig +from diffsynth.diffusion import * +from diffsynth.utils.lora.sdxl import SdxlLoRAConverter +os.environ["TOKENIZERS_PARALLELISM"] = "false" + + +class StableDiffusionXLTrainingModule(DiffusionTrainingModule): + def __init__( + self, + model_paths=None, model_id_with_origin_paths=None, + tokenizer_path=None, + trainable_models=None, + lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None, + preset_lora_path=None, preset_lora_model=None, + use_gradient_checkpointing=True, + use_gradient_checkpointing_offload=False, + extra_inputs=None, + fp8_models=None, + offload_models=None, + resume_from_checkpoint=None, remove_prefix_in_ckpt=None, + device="cpu", + task="sft", + ): + super().__init__() + # Load models + model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, fp8_models=fp8_models, offload_models=offload_models, device=device) + tokenizer_config = self.parse_path_or_model_id(tokenizer_path, ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="tokenizer/")) + tokenizer_2_config = self.parse_path_or_model_id(tokenizer_path, ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="tokenizer_2/")) + self.pipe = StableDiffusionXLPipeline.from_pretrained(torch_dtype=torch.float32, device=device, model_configs=model_configs, tokenizer_config=tokenizer_config, tokenizer_2_config=tokenizer_2_config) + self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model) + self.resume_from_checkpoint(resume_from_checkpoint, remove_prefix_in_ckpt) + + # Training mode + self.switch_pipe_to_training_mode( + self.pipe, trainable_models, + lora_base_model, lora_target_modules, lora_rank, lora_checkpoint, + preset_lora_path, preset_lora_model, + task=task, + ) + + # Other configs + self.use_gradient_checkpointing = use_gradient_checkpointing + self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload + self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else [] + self.fp8_models = fp8_models + self.task = task + self.task_to_loss = { + "sft:data_process": lambda pipe, *args: args, + "direct_distill:data_process": lambda pipe, *args: args, + "sft": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi), + "sft:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi), + "direct_distill": lambda pipe, inputs_shared, inputs_posi, inputs_nega: DirectDistillLoss(pipe, **inputs_shared, **inputs_posi), + "direct_distill:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: DirectDistillLoss(pipe, **inputs_shared, **inputs_posi), + } + + def get_pipeline_inputs(self, data): + inputs_posi = {"prompt": data["prompt"]} + inputs_nega = {"negative_prompt": ""} + inputs_shared = { + # Assume you are using this pipeline for inference, + # please fill in the input parameters. + "input_image": data["image"], + "height": data["image"].size[1], + "width": data["image"].size[0], + # Please do not modify the following parameters + # unless you clearly know what this will cause. + "cfg_scale": 1, + "rand_device": self.pipe.device, + "use_gradient_checkpointing": self.use_gradient_checkpointing, + "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload, + } + inputs_shared = self.parse_extra_inputs(data, self.extra_inputs, inputs_shared) + return inputs_shared, inputs_posi, inputs_nega + + def forward(self, data, inputs=None): + if inputs is None: inputs = self.get_pipeline_inputs(data) + inputs = self.transfer_data_to_device(inputs, self.pipe.device, self.pipe.torch_dtype) + for unit in self.pipe.units: + inputs = self.pipe.unit_runner(unit, self.pipe, *inputs) + loss = self.task_to_loss[self.task](self.pipe, *inputs) + return loss + + +def parser(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser = add_general_config(parser) + parser = add_image_size_config(parser) + parser.add_argument("--tokenizer_path", type=str, default=None, help="Path to tokenizer.") + parser.add_argument("--tokenizer_2_path", type=str, default=None, help="Path to tokenizer 2.") + parser.add_argument("--align_to_opensource_format", default=False, action="store_true", help="Whether to align the lora format to opensource format.") + return parser + + +if __name__ == "__main__": + parser = parser() + args = parser.parse_args() + accelerator = accelerate.Accelerator( + gradient_accumulation_steps=args.gradient_accumulation_steps, + kwargs_handlers=[accelerate.DistributedDataParallelKwargs(find_unused_parameters=args.find_unused_parameters)], + ) + dataset = UnifiedDataset( + base_path=args.dataset_base_path, + metadata_path=args.dataset_metadata_path, + repeat=args.dataset_repeat, + data_file_keys=args.data_file_keys.split(","), + main_data_operator=UnifiedDataset.default_image_operator( + base_path=args.dataset_base_path, + max_pixels=args.max_pixels, + height=args.height, + width=args.width, + height_division_factor=32, + width_division_factor=32, + ) + ) + model = StableDiffusionXLTrainingModule( + model_paths=args.model_paths, + model_id_with_origin_paths=args.model_id_with_origin_paths, + tokenizer_path=args.tokenizer_path, + trainable_models=args.trainable_models, + lora_base_model=args.lora_base_model, + lora_target_modules=args.lora_target_modules, + lora_rank=args.lora_rank, + lora_checkpoint=args.lora_checkpoint, + preset_lora_path=args.preset_lora_path, + preset_lora_model=args.preset_lora_model, + use_gradient_checkpointing=args.use_gradient_checkpointing, + use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload, + extra_inputs=args.extra_inputs, + fp8_models=args.fp8_models, + offload_models=args.offload_models, + resume_from_checkpoint=args.resume_from_checkpoint, + remove_prefix_in_ckpt=args.remove_prefix_in_ckpt, + task=args.task, + device="cpu" if args.enable_model_cpu_offload else accelerator.device, + ) + model_logger = ModelLogger( + args.output_path, + remove_prefix_in_ckpt=args.remove_prefix_in_ckpt, + state_dict_converter=SdxlLoRAConverter.align_to_opensource_format if args.align_to_opensource_format else lambda x:x, + enable_tensorboard_log=args.enable_tensorboard_log, + enable_swanlab_log=args.enable_swanlab_log, + swanlab_project=args.swanlab_project, + enable_wandb_log=args.enable_wandb_log, + wandb_project=args.wandb_project, + ) + launcher_map = { + "sft:data_process": launch_data_process_task, + "direct_distill:data_process": launch_data_process_task, + "sft": launch_training_task, + "sft:train": launch_training_task, + "direct_distill": launch_training_task, + "direct_distill:train": launch_training_task, + } + launcher_map[args.task](accelerator, dataset, model, model_logger, args=args) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_training/validate_full/stable-diffusion-xl-base-1.0.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_training/validate_full/stable-diffusion-xl-base-1.0.py new file mode 100644 index 0000000000000000000000000000000000000000..06062ceb4f89baee0ce063f2952fb0bba051abdc --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_training/validate_full/stable-diffusion-xl-base-1.0.py @@ -0,0 +1,28 @@ +from diffsynth.pipelines.stable_diffusion_xl import StableDiffusionXLPipeline, ModelConfig +from diffsynth.core import load_state_dict +import torch + +pipe = StableDiffusionXLPipeline.from_pretrained( + torch_dtype=torch.float32, + model_configs=[ + ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="text_encoder_2/model.safetensors"), + ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="unet/diffusion_pytorch_model.safetensors"), + ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="tokenizer/"), + tokenizer_2_config=ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="tokenizer_2/"), +) +state_dict = load_state_dict("./models/train/stable-diffusion-xl-base-1.0_full/epoch-1.safetensors", torch_dtype=torch.float32) +pipe.unet.load_state_dict(state_dict) + +image = pipe( + prompt="a dog", + negative_prompt="", + cfg_scale=7.0, + height=1024, + width=1024, + seed=42, + num_inference_steps=50, +) +image.save("image_stable-diffusion-xl-base-1.0_full.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_training/validate_lora/stable-diffusion-xl-base-1.0.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_training/validate_lora/stable-diffusion-xl-base-1.0.py new file mode 100644 index 0000000000000000000000000000000000000000..425e95a44faba867bc3c60f735cb02068eabd076 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/stable_diffusion_xl/model_training/validate_lora/stable-diffusion-xl-base-1.0.py @@ -0,0 +1,27 @@ +import torch +from diffsynth.core import ModelConfig +from diffsynth.pipelines.stable_diffusion_xl import StableDiffusionXLPipeline + +pipe = StableDiffusionXLPipeline.from_pretrained( + torch_dtype=torch.float32, + model_configs=[ + ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="text_encoder_2/model.safetensors"), + ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="unet/diffusion_pytorch_model.safetensors"), + ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="tokenizer/"), + tokenizer_2_config=ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="tokenizer_2/"), +) +pipe.load_lora(pipe.unet, "models/train/stable-diffusion-xl-base-1.0_lora/epoch-4.safetensors") + +image = pipe( + prompt="a dog", + negative_prompt="", + cfg_scale=7.0, + height=1024, + width=1024, + seed=42, + num_inference_steps=50, +) +image.save("image_stable-diffusion-xl-base-1.0.jpg") diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/README.md b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..71798325d5fd9b3aca5592854b097d66d8f5ee1a --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/README.md @@ -0,0 +1,3 @@ +English Document: https://diffsynth-studio-doc.readthedocs.io/en/latest/Model_Details/Wan.html + +中文文档：https://diffsynth-studio-doc.readthedocs.io/zh-cn/latest/Model_Details/Wan.html diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/acceleration/unified_sequence_parallel.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/acceleration/unified_sequence_parallel.py new file mode 100644 index 0000000000000000000000000000000000000000..39983ef44bfa949afe987dcb7e763ce907a45e1d --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/acceleration/unified_sequence_parallel.py @@ -0,0 +1,26 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +import torch.distributed as dist + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + use_usp=True, + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +# Text-to-video +video = pipe( + prompt="一名宇航员身穿太空服，面朝镜头骑着一匹机械马在火星表面驰骋。红色的荒凉地表延伸至远方，点缀着巨大的陨石坑和奇特的岩石结构。机械马的步伐稳健，扬起微弱的尘埃，展现出未来科技与原始探索的完美结合。宇航员手持操控装置，目光坚定，仿佛正在开辟人类的新疆域。背景是深邃的宇宙和蔚蓝的地球，画面既科幻又充满希望，让人不禁畅想未来的星际生活。", + 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", + seed=0, tiled=True, +) +if dist.get_rank() == 0: + save_video(video, "video1.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/LongCat-Video.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/LongCat-Video.py new file mode 100644 index 0000000000000000000000000000000000000000..a064e91c04f9d01575af76baf8ea2af148be3d1e --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/LongCat-Video.py @@ -0,0 +1,35 @@ +import torch +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="meituan-longcat/LongCat-Video", origin_file_pattern="dit/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +# Text-to-video +video = pipe( + prompt="In a realistic photography style, a white boy around seven or eight years old sits on a park bench, wearing a light blue T-shirt, denim shorts, and white sneakers. He holds an ice cream cone with vanilla and chocolate flavors, and beside him is a medium-sized golden Labrador. Smiling, the boy offers the ice cream to the dog, who eagerly licks it with its tongue. The sun is shining brightly, and the background features a green lawn and several tall trees, creating a warm and loving scene.", + negative_prompt="Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards", + seed=0, tiled=True, num_frames=93, + cfg_scale=2, sigma_shift=1, +) +save_video(video, "video_1_LongCat-Video.mp4", fps=15, quality=5) + +# Video-continuation (The number of frames in `longcat_video` should be 4n+1.) +longcat_video = video[-17:] +video = pipe( + prompt="In a realistic photography style, a white boy around seven or eight years old sits on a park bench, wearing a light blue T-shirt, denim shorts, and white sneakers. He holds an ice cream cone with vanilla and chocolate flavors, and beside him is a medium-sized golden Labrador. Smiling, the boy offers the ice cream to the dog, who eagerly licks it with its tongue. The sun is shining brightly, and the background features a green lawn and several tall trees, creating a warm and loving scene.", + negative_prompt="Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards", + seed=1, tiled=True, num_frames=93, + cfg_scale=2, sigma_shift=1, + longcat_video=longcat_video, +) +save_video(video, "video_2_LongCat-Video.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Video-As-Prompt-Wan2.1-14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Video-As-Prompt-Wan2.1-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..ec23b02df046b7c375556cb6cc9168e567904a26 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Video-As-Prompt-Wan2.1-14B.py @@ -0,0 +1,49 @@ +import torch +import PIL +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download +from typing import List + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="ByteDance/Video-As-Prompt-Wan2.1-14B", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +dataset_snapshot_download("DiffSynth-Studio/example_video_dataset", allow_file_pattern="wanvap/*", local_dir="data/example_video_dataset") +ref_video_path = 'data/example_video_dataset/wanvap/vap_ref.mp4' +target_image_path = 'data/example_video_dataset/wanvap/input_image.jpg' + +def select_frames(video_frames, num): + idx = torch.linspace(0, len(video_frames) - 1, num).long().tolist() + return [video_frames[i] for i in idx] + +image = Image.open(target_image_path).convert("RGB") +ref_video = VideoData(ref_video_path, height=480, width=832) +ref_frames = select_frames(ref_video, num=49) + +vap_prompt = "A man stands with his back to the camera on a dirt path overlooking sun-drenched, rolling green tea plantations. He wears a blue and green plaid shirt, dark pants, and white shoes. As he turns to face the camera and spreads his arms, a brief, magical burst of sparkling golden light particles envelops him. Through this shimmer, he seamlessly transforms into a Labubu toy character. His head morphs into the iconic large, furry-eared head of the toy, featuring a wide grin with pointed teeth and red cheek markings. The character retains the man's original plaid shirt and clothing, which now fit its stylized, cartoonish body. The camera remains static throughout the transformation, positioned low among the tea bushes, maintaining a consistent view of the subject and the expansive scenery." +prompt = "A young woman with curly hair, wearing a green hijab and a floral dress, plays a violin in front of a vintage green car on a tree-lined street. She executes a swift counter-clockwise turn to face the camera. During the turn, a brilliant shower of golden, sparkling particles erupts and momentarily obscures her figure. As the particles fade, she is revealed to have seamlessly transformed into a Labubu toy character. This new figure, now with the toy's signature large ears, big eyes, and toothy grin, maintains the original pose and continues playing the violin. The character's clothing—the green hijab, floral dress, and black overcoat—remains identical to the woman's. Throughout this transition, the camera stays static, and the street-side environment remains completely consistent." +negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards" + +video = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + input_image=image, + seed=42, tiled=True, + height=480, width=832, + num_frames=49, + vap_video=ref_frames, + vap_prompt=vap_prompt, + negative_vap_prompt=negative_prompt, +) +save_video(video, "video_Video-As-Prompt-Wan2.1-14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan-Dancer-14B-global.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan-Dancer-14B-global.py new file mode 100644 index 0000000000000000000000000000000000000000..10b54cf6e592605eb9e23a837113dc4b599944ef --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan-Dancer-14B-global.py @@ -0,0 +1,48 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="global_model.safetensors"), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) +dataset_snapshot_download( + "DiffSynth-Studio/diffsynth_example_dataset", + local_dir="data/diffsynth_example_dataset", + allow_file_pattern="wanvideo/Wan-Dancer-14B-global/*" +) +# This is a specialized model with the following constraints on its input parameters: +# * The model outputs a sequence of keyframes rather than a video; therefore, `framewise_decoding=True` must be set. +# * When the number of keyframes is $n$, `num_frames` = 4 * (n - 1) + 1. +# * Reducing `height`, `width`, `num_frames`, or `num_inference_steps` may lead to severe artifacts or generation failure. +# * The audio file specified by `wantodance_music_path` must match the video duration, calculated as (`num_frames` / 7.5) seconds. +# * The width and height of `wantodance_reference_image` must be multiples of 16. +# * `wantodance_fps` is configurable, but since the model appears to have been trained exclusively at 7.5 FPS, setting it to other values is not recommended. +# * The first frame of `wantodance_keyframes` is the `wantodance_reference_image`, while all subsequent frames are solid black. +# * `wantodance_keyframes_mask` indicates the positions of valid frames within `wantodance_keyframes`. +wantodance_keyframes = VideoData("data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-global/keyframes.mp4") +wantodance_keyframes = [wantodance_keyframes[i] for i in range(149)] +video = pipe( + prompt="一个人正在跳舞，舞蹈种类是韩舞。帧率是7.5000", + 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", + seed=0, tiled=False, + height=1280, width=720, num_frames=149, + num_inference_steps=48, + wantodance_music_path="data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-global/music.WAV", + wantodance_reference_image=Image.open("data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-global/refimage.jpg"), + wantodance_fps=7.5, + wantodance_keyframes=wantodance_keyframes, + wantodance_keyframes_mask=[1] + [0] * 148, + framewise_decoding=True, +) +save_video(video, "video_Wan-Dancer-14B-global.mp4", fps=7.5, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan-Dancer-14B-local.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan-Dancer-14B-local.py new file mode 100644 index 0000000000000000000000000000000000000000..40883cbf1e1aec4e6bebcccbf5cbc24975691079 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan-Dancer-14B-local.py @@ -0,0 +1,52 @@ +import torch, os +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="local_model.safetensors"), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) +dataset_snapshot_download( + "DiffSynth-Studio/diffsynth_example_dataset", + local_dir="data/diffsynth_example_dataset", + allow_file_pattern="wanvideo/Wan-Dancer-14B-local/*" +) +# This is a specialized model with the following constraints on its input parameters: +# * The model renders and outputs video based on a sequence of keyframes; therefore, `wantodance_keyframes` must be provided correctly. +# * If you need to generate a long video, please generate it in segments, and ensure that `wantodance_music_path`, `wantodance_keyframes`, and `wantodance_keyframes_mask` are properly split accordingly. +# * The audio file specified by `wantodance_music_path` must match the video duration, calculated as (`num_frames` / 30) seconds. +# * The width and height of `wantodance_reference_image` must be multiples of 16. +# * `wantodance_fps` is configurable, but since the model appears to have been trained exclusively at 30 FPS, setting it to other values is not recommended. +# * In `wantodance_keyframes`, frames that are not keyframes should be solid black. +# * `wantodance_keyframes_mask` indicates the positions of valid frames within `wantodance_keyframes`. +wantodance_keyframes = VideoData("data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-local/keyframes.mp4") +wantodance_keyframes = [wantodance_keyframes[i] for i in range(149)] +video = pipe( + prompt="一个人正在跳舞，舞蹈种类是古典舞,图像清晰程度高,人物动作平均幅度中等,人物动作最大幅度中等。, 帧率是30fps。", + 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", + seed=0, tiled=True, + height=1280, width=720, num_frames=149, + num_inference_steps=24, + wantodance_music_path="data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-local/music.wav", + wantodance_reference_image=Image.open("data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-local/refimage.jpg"), + wantodance_fps=30, + wantodance_keyframes=wantodance_keyframes, + wantodance_keyframes_mask=[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1], +) +save_video(video, "video_Wan-Dancer-14B-local.mp4", fps=30, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py new file mode 100644 index 0000000000000000000000000000000000000000..a6292d920321d23905034c9f96caa7a91c35c245 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py @@ -0,0 +1,34 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1", origin_file_pattern="model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +# Text-to-video +video = pipe( + prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。", + 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", + seed=1, tiled=True, + motion_bucket_id=0 +) +save_video(video, "video_slow_Wan2.1-1.3b-speedcontrol-v1.mp4", fps=15, quality=5) + +video = pipe( + prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。", + 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", + seed=1, tiled=True, + motion_bucket_id=100 +) +save_video(video, "video_fast_Wan2.1-1.3b-speedcontrol-v1.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py new file mode 100644 index 0000000000000000000000000000000000000000..fa9a899e807b8247c1da0c953466e0fd0fad93ad --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py @@ -0,0 +1,36 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/first_frame.jpeg", "data/examples/wan/last_frame.jpeg"] +) + +# First and last frame to video +video = pipe( + prompt="写实风格，一个女生手持枯萎的花站在花园中，镜头逐渐拉远，记录下花园的全貌。", + 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", + input_image=Image.open("data/examples/wan/first_frame.jpeg").resize((960, 960)), + end_image=Image.open("data/examples/wan/last_frame.jpeg").resize((960, 960)), + seed=0, tiled=True, + height=960, width=960, num_frames=33, + sigma_shift=16, +) +save_video(video, "video_Wan2.1-FLF2V-14B-720P.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..748fba780e4eaf6fdaa886de3f263077ebea4149 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py @@ -0,0 +1,34 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/control_video.mp4" +) + +# Control video +control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576) +video = pipe( + prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。", + 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", + control_video=control_video, height=832, width=576, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-1.3B-Control.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..39324bad0dc5c3ec7007498f025433e0b0b917bb --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py @@ -0,0 +1,36 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# First and last frame to video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + input_image=image, + seed=0, tiled=True + # You can input `end_image=xxx` to control the last frame of the video. + # The model will automatically generate the dynamic content between `input_image` and `end_image`. +) +save_video(video, "video_Wan2.1-Fun-1.3B-InP.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..b8334fddee746bb36aa5c6ee7e566459bd45a5c4 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py @@ -0,0 +1,34 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/control_video.mp4" +) + +# Control video +control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576) +video = pipe( + prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。", + 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", + control_video=control_video, height=832, width=576, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-14B-Control.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..fe16080400ecb4b07ceb4ad3efd09d984b8643f0 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py @@ -0,0 +1,36 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# First and last frame to video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + input_image=image, + seed=0, tiled=True + # You can input `end_image=xxx` to control the last frame of the video. + # The model will automatically generate the dynamic content between `input_image` and `end_image`. +) +save_video(video, "video_Wan2.1-Fun-14B-InP.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..7babad1901eb3551dff31735d6aa598a8cc88ed6 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py @@ -0,0 +1,44 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +input_image = Image.open("data/examples/wan/input_image.jpg") + +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + seed=0, tiled=True, + input_image=input_image, + camera_control_direction="Left", camera_control_speed=0.01, +) +save_video(video, "video_left_Wan2.1-Fun-V1.1-1.3B-Control-Camera.mp4", fps=15, quality=5) + +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + seed=0, tiled=True, + input_image=input_image, + camera_control_direction="Up", camera_control_speed=0.01, +) +save_video(video, "video_up_Wan2.1-Fun-V1.1-1.3B-Control-Camera.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..acfedaea41d6d2546b49b95c8ee1124303d27734 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py @@ -0,0 +1,36 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/control_video.mp4", "data/examples/wan/reference_image_girl.png"] +) + +# Control video +control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576) +reference_image = Image.open("data/examples/wan/reference_image_girl.png").resize((576, 832)) +video = pipe( + prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。", + 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", + control_video=control_video, reference_image=reference_image, + height=832, width=576, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-1.3B-Control.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..1d14badbc6d5aab9d821e4dc6deb29e83bff356b --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py @@ -0,0 +1,36 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# First and last frame to video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + input_image=image, + seed=0, tiled=True + # You can input `end_image=xxx` to control the last frame of the video. + # The model will automatically generate the dynamic content between `input_image` and `end_image`. +) +save_video(video, "video_Wan2.1-Fun-V1.1-1.3B-InP.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..019b1dac57343c506d14082e02889201465e8faa --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py @@ -0,0 +1,44 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +input_image = Image.open("data/examples/wan/input_image.jpg") + +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + seed=0, tiled=True, + input_image=input_image, + camera_control_direction="Left", camera_control_speed=0.01, +) +save_video(video, "video_left_Wan2.1-Fun-V1.1-14B-Control-Camera.mp4", fps=15, quality=5) + +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + seed=0, tiled=True, + input_image=input_image, + camera_control_direction="Up", camera_control_speed=0.01, +) +save_video(video, "video_up_Wan2.1-Fun-V1.1-14B-Control-Camera.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..45421d99a49f4ef9ab056bca257b0aa702c4319b --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py @@ -0,0 +1,36 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/control_video.mp4", "data/examples/wan/reference_image_girl.png"] +) + +# Control video +control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576) +reference_image = Image.open("data/examples/wan/reference_image_girl.png").resize((576, 832)) +video = pipe( + prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。", + 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", + control_video=control_video, reference_image=reference_image, + height=832, width=576, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-14B-Control.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..a8a87352c54b9aa23627afa89e2a87b5164c88b3 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py @@ -0,0 +1,36 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# First and last frame to video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + input_image=image, + seed=0, tiled=True + # You can input `end_image=xxx` to control the last frame of the video. + # The model will automatically generate the dynamic content between `input_image` and `end_image`. +) +save_video(video, "video_Wan2.1-Fun-V1.1-14B-InP.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py new file mode 100644 index 0000000000000000000000000000000000000000..d3d22cebec198848b26933fd70019dfa3d5db5fc --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py @@ -0,0 +1,34 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# Image-to-video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + input_image=image, + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-I2V-14B-480P.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py new file mode 100644 index 0000000000000000000000000000000000000000..3220433cf64cecf3d2f8cb3e8b1b3e4cb054b9c7 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py @@ -0,0 +1,35 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# Image-to-video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + input_image=image, + seed=0, tiled=True, + height=720, width=1280, +) +save_video(video, "video_Wan2.1-I2V-14B-720P.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..747fe4e1896165f63807df4306e5521b13790cf9 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py @@ -0,0 +1,34 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +# Text-to-video +video = pipe( + prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。", + 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", + seed=0, tiled=True, +) +save_video(video, "video_1_Wan2.1-T2V-1.3B.mp4", fps=15, quality=5) + +# Video-to-video +video = VideoData("video_1_Wan2.1-T2V-1.3B.mp4", height=480, width=832) +video = pipe( + prompt="纪实摄影风格画面，一只活泼的小狗戴着黑色墨镜在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，戴着黑色墨镜，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。", + 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", + input_video=video, denoising_strength=0.7, + seed=1, tiled=True +) +save_video(video, "video_2_Wan2.1-T2V-1.3B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-T2V-14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-T2V-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..018571b84b10c2a0b22e3a54869a306fb83c75d0 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-T2V-14B.py @@ -0,0 +1,24 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +# Text-to-video +video = pipe( + prompt="一名宇航员身穿太空服，面朝镜头骑着一匹机械马在火星表面驰骋。红色的荒凉地表延伸至远方，点缀着巨大的陨石坑和奇特的岩石结构。机械马的步伐稳健，扬起微弱的尘埃，展现出未来科技与原始探索的完美结合。宇航员手持操控装置，目光坚定，仿佛正在开辟人类的新疆域。背景是深邃的宇宙和蔚蓝的地球，画面既科幻又充满希望，让人不禁畅想未来的星际生活。", + 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", + seed=0, tiled=True, +) +save_video(video, "video_Wan2.1-T2V-14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py new file mode 100644 index 0000000000000000000000000000000000000000..aed089d6190ecdd645c19b4dafc1731464e6e3a7 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py @@ -0,0 +1,52 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="Wan2.1_VAE.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/depth_video.mp4", "data/examples/wan/cat_fightning.jpg"] +) + +# Depth video -> Video +control_video = VideoData("data/examples/wan/depth_video.mp4", height=480, width=832) +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_video=control_video, + seed=1, tiled=True +) +save_video(video, "video_1_Wan2.1-VACE-1.3B-Preview.mp4", fps=15, quality=5) + +# Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video_2_Wan2.1-VACE-1.3B-Preview.mp4", fps=15, quality=5) + +# Depth video + Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_video=control_video, + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video_3_Wan2.1-VACE-1.3B-Preview.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..6e46d984e0d2902234ab7b33667fd44e2ec0b6d8 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py @@ -0,0 +1,53 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="Wan2.1_VAE.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/depth_video.mp4", "data/examples/wan/cat_fightning.jpg"] +) + +# Depth video -> Video +control_video = VideoData("data/examples/wan/depth_video.mp4", height=480, width=832) +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_video=control_video, + seed=1, tiled=True +) +save_video(video, "video_1_Wan2.1-VACE-1.3B.mp4", fps=15, quality=5) + +# Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video_2_Wan2.1-VACE-1.3B.mp4", fps=15, quality=5) + +# Depth video + Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_video=control_video, + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video_3_Wan2.1-VACE-1.3B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-VACE-14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-VACE-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..e4f8474b90c662eb6c8e882700228061694efb53 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.1-VACE-14B.py @@ -0,0 +1,54 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + + + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/depth_video.mp4", "data/examples/wan/cat_fightning.jpg"] +) + +# Depth video -> Video +control_video = VideoData("data/examples/wan/depth_video.mp4", height=480, width=832) +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_video=control_video, + seed=1, tiled=True +) +save_video(video, "video_1_Wan2.1-VACE-14B.mp4", fps=15, quality=5) + +# Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video_2_Wan2.1-VACE-14B.mp4", fps=15, quality=5) + +# Depth video + Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_video=control_video, + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video_3_Wan2.1-VACE-14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-Animate-14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-Animate-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..d435b688ffc760f8606ed9380952f6866d31cde6 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-Animate-14B.py @@ -0,0 +1,62 @@ +import torch +from PIL import Image +from diffsynth.core import load_state_dict +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download, snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern="data/examples/wan/animate/*", +) + +# Animate +input_image = Image.open("data/examples/wan/animate/animate_input_image.png") +animate_pose_video = VideoData("data/examples/wan/animate/animate_pose_video.mp4").raw_data()[:81-4] +animate_face_video = VideoData("data/examples/wan/animate/animate_face_video.mp4").raw_data()[:81-4] +video = pipe( + prompt="视频中的人在做动作", + seed=0, tiled=True, + input_image=input_image, + animate_pose_video=animate_pose_video, + animate_face_video=animate_face_video, + num_frames=81, height=720, width=1280, + num_inference_steps=20, cfg_scale=1, +) +save_video(video, "video_1_Wan2.2-Animate-14B.mp4", fps=15, quality=5) + +# Replace +snapshot_download("Wan-AI/Wan2.2-Animate-14B", allow_file_pattern="relighting_lora.ckpt", local_dir="models/Wan-AI/Wan2.2-Animate-14B") +lora_state_dict = load_state_dict("models/Wan-AI/Wan2.2-Animate-14B/relighting_lora.ckpt", torch_dtype=torch.bfloat16, device="cuda")["state_dict"] +pipe.load_lora(pipe.dit, state_dict=lora_state_dict) +input_image = Image.open("data/examples/wan/animate/replace_input_image.png") +animate_pose_video = VideoData("data/examples/wan/animate/replace_pose_video.mp4").raw_data()[:81-4] +animate_face_video = VideoData("data/examples/wan/animate/replace_face_video.mp4").raw_data()[:81-4] +animate_inpaint_video = VideoData("data/examples/wan/animate/replace_inpaint_video.mp4").raw_data()[:81-4] +animate_mask_video = VideoData("data/examples/wan/animate/replace_mask_video.mp4").raw_data()[:81-4] +video = pipe( + prompt="视频中的人在做动作", + seed=0, tiled=True, + input_image=input_image, + animate_pose_video=animate_pose_video, + animate_face_video=animate_face_video, + animate_inpaint_video=animate_inpaint_video, + animate_mask_video=animate_mask_video, + num_frames=81, height=720, width=1280, + num_inference_steps=20, cfg_scale=1, +) +save_video(video, "video_2_Wan2.2-Animate-14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..58e3b426dca4126a8d8b0c9ed90e73c0e290d59e --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py @@ -0,0 +1,43 @@ +import torch +from diffsynth.utils.data import save_video,VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from PIL import Image +from modelscope import dataset_snapshot_download + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +input_image = Image.open("data/examples/wan/input_image.jpg") + +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + seed=0, tiled=True, + input_image=input_image, + camera_control_direction="Left", camera_control_speed=0.01, +) +save_video(video, "video_left_Wan2.2-Fun-A14B-Control-Camera.mp4", fps=15, quality=5) + +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + seed=0, tiled=True, + input_image=input_image, + camera_control_direction="Up", camera_control_speed=0.01, +) +save_video(video, "video_up_Wan2.2-Fun-A14B-Control-Camera.mp4", fps=15, quality=5) \ No newline at end of file diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..e9505804e5ca15b46fecb630f0c715afc0642334 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py @@ -0,0 +1,35 @@ +import torch +from diffsynth.utils.data import save_video,VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from PIL import Image +from modelscope import dataset_snapshot_download + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="Wan2.1_VAE.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/control_video.mp4", "data/examples/wan/reference_image_girl.png"] +) + +# Control video +control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576) +reference_image = Image.open("data/examples/wan/reference_image_girl.png").resize((576, 832)) +video = pipe( + prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。", + 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", + control_video=control_video, reference_image=reference_image, + height=832, width=576, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.2-Fun-A14B-Control.mp4", fps=15, quality=5) \ No newline at end of file diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..4458de1dbc5e858d4cd44e21f4ebb5d3b2181b0a --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py @@ -0,0 +1,35 @@ +import torch +from diffsynth.utils.data import save_video +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from PIL import Image +from modelscope import dataset_snapshot_download + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="Wan2.1_VAE.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# First and last frame to video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + input_image=image, + seed=0, tiled=True, + # You can input `end_image=xxx` to control the last frame of the video. + # The model will automatically generate the dynamic content between `input_image` and `end_image`. +) +save_video(video, "video_Wan2.2-Fun-A14B-InP.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py new file mode 100644 index 0000000000000000000000000000000000000000..b95d6112294ba3176c33d3cd5f964afcf02b2293 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py @@ -0,0 +1,33 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/cat_fightning.jpg"] +) +input_image = Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)) + +video = pipe( + prompt="Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.", + 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", + seed=0, tiled=True, + input_image=input_image, + switch_DiT_boundary=0.9, +) +save_video(video, "video_Wan2.2-I2V-A14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-S2V-14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-S2V-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..140c6a0d6d062040c275f45e7d81e3153912d0c9 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-S2V-14B.py @@ -0,0 +1,73 @@ +# This script can generate a single video clip. +# If you need generate long videos, please refer to `Wan2.2-S2V-14B_multi_clips.py`. +import torch +from PIL import Image +import librosa +from diffsynth.utils.data import VideoData, save_video_with_audio +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/model.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + audio_processor_config=ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/"), +) +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_video_dataset", + local_dir="./data/example_video_dataset", + allow_file_pattern=f"wans2v/*" +) + +num_frames = 81 # 4n+1 +height = 448 +width = 832 + +prompt = "a person is singing" +negative_prompt = "画面模糊，最差质量，画面模糊，细节模糊不清，情绪激动剧烈，手快速抖动，字幕，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" +input_image = Image.open("data/example_video_dataset/wans2v/pose.png").convert("RGB").resize((width, height)) +# s2v audio input, recommend 16kHz sampling rate +audio_path = 'data/example_video_dataset/wans2v/sing.MP3' +input_audio, sample_rate = librosa.load(audio_path, sr=16000) + +# Speech-to-video +video = pipe( + prompt=prompt, + input_image=input_image, + negative_prompt=negative_prompt, + seed=0, + num_frames=num_frames, + height=height, + width=width, + audio_sample_rate=sample_rate, + input_audio=input_audio, + num_inference_steps=40, +) +save_video_with_audio(video[1:], "video_1_Wan2.2-S2V-14B.mp4", audio_path, fps=16, quality=5) + +# s2v will use the first (num_frames) frames as reference. height and width must be the same as input_image. And fps should be 16, the same as output video fps. +pose_video_path = 'data/example_video_dataset/wans2v/pose.mp4' +pose_video = VideoData(pose_video_path, height=height, width=width) + +# Speech-to-video with pose +video = pipe( + prompt=prompt, + input_image=input_image, + negative_prompt=negative_prompt, + seed=0, + num_frames=num_frames, + height=height, + width=width, + audio_sample_rate=sample_rate, + input_audio=input_audio, + s2v_pose_video=pose_video, + num_inference_steps=40, +) +save_video_with_audio(video[1:], "video_2_Wan2.2-S2V-14B.mp4", audio_path, fps=16, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py new file mode 100644 index 0000000000000000000000000000000000000000..43da5b697f726cd1aec8aae631d3fe25f228cfc5 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py @@ -0,0 +1,124 @@ +import torch +from PIL import Image +import librosa +from diffsynth.utils.data import VideoData, save_video_with_audio +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig, WanVideoUnit_S2V +from modelscope import dataset_snapshot_download + + +def speech_to_video( + prompt, + input_image, + audio_path, + negative_prompt="", + num_clip=None, + audio_sample_rate=16000, + pose_video_path=None, + infer_frames=80, + height=448, + width=832, + num_inference_steps=40, + fps=16, # recommend fixing fps as 16 for s2v + motion_frames=73, # hyperparameter of wan2.2-s2v + save_path=None, +): + # s2v audio input, recommend 16kHz sampling rate + input_audio, sample_rate = librosa.load(audio_path, sr=audio_sample_rate) + # s2v will use the first (num_frames) frames as reference. height and width must be the same as input_image. And fps should be 16, the same as output video fps. + pose_video = VideoData(pose_video_path, height=height, width=width) if pose_video_path is not None else None + + with torch.no_grad(): + audio_embeds, pose_latents, num_repeat = WanVideoUnit_S2V.pre_calculate_audio_pose( + pipe=pipe, + input_audio=input_audio, + audio_sample_rate=sample_rate, + s2v_pose_video=pose_video, + num_frames=infer_frames + 1, + height=height, + width=width, + fps=fps, + ) + num_repeat = min(num_repeat, num_clip) if num_clip is not None else num_repeat + print(f"Generating {num_repeat} video clips...") + motion_video = None + video = [] + for r in range(num_repeat): + s2v_pose_latents = pose_latents[r] if pose_latents is not None else None + current_clip_tensor = pipe( + prompt=prompt, + input_image=input_image, + negative_prompt=negative_prompt, + seed=0, + num_frames=infer_frames + 1, + height=height, + width=width, + audio_embeds=audio_embeds[r], + s2v_pose_latents=s2v_pose_latents, + motion_video=motion_video, + num_inference_steps=num_inference_steps, + output_type="floatpoint", + ) + # (B, C, T, H, W) + current_clip_tensor = current_clip_tensor[:,:,-infer_frames:,:,:] + if r == 0: + current_clip_tensor = current_clip_tensor[:,:,3:,:,:] + overlap_frames_num = min(motion_frames, current_clip_tensor.shape[2]) + motion_video = current_clip_tensor[:,:,-overlap_frames_num:,:,:].clone() + else: + overlap_frames_num = min(motion_frames, current_clip_tensor.shape[2]) + motion_video = torch.cat((motion_video[:,:,overlap_frames_num:,:,:], current_clip_tensor[:,:,-overlap_frames_num:,:,:]), dim=2) + current_clip_quantized = pipe.vae_output_to_video(current_clip_tensor) + video.extend(current_clip_quantized) + save_video_with_audio(video, save_path, audio_path, fps=16, quality=5) + print(f"processed the {r+1}th clip of total {num_repeat} clips.") + return video + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/model.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + audio_processor_config=ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_video_dataset", + local_dir="./data/example_video_dataset", + allow_file_pattern=f"wans2v/*", +) + +infer_frames = 80 # 4n +height = 448 +width = 832 + +prompt = "a person is singing" +negative_prompt = "画面模糊，最差质量，画面模糊，细节模糊不清，情绪激动剧烈，手快速抖动，字幕，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" +input_image = Image.open("data/example_video_dataset/wans2v/pose.png").convert("RGB").resize((width, height)) + +video_with_audio = speech_to_video( + prompt=prompt, + input_image=input_image, + audio_path='data/example_video_dataset/wans2v/sing.MP3', + negative_prompt=negative_prompt, + pose_video_path='data/example_video_dataset/wans2v/pose.mp4', + save_path="video_full_Wan2.2-S2V-14B.mp4", + infer_frames=infer_frames, + height=height, + width=width, +) +# num_clip means generating only the first n clips with n * infer_frames frames. +video_with_audio_pose = speech_to_video( + prompt=prompt, + input_image=input_image, + audio_path='data/example_video_dataset/wans2v/sing.MP3', + negative_prompt=negative_prompt, + pose_video_path='data/example_video_dataset/wans2v/pose.mp4', + save_path="video_clip_2_Wan2.2-S2V-14B.mp4", + num_clip=2 +) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py new file mode 100644 index 0000000000000000000000000000000000000000..9bef43217574f751c01cb4c4de5e602bc113a3de --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py @@ -0,0 +1,24 @@ +import torch +from diffsynth.utils.data import save_video +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +# Text-to-video +video = pipe( + prompt="Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.", + 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", + seed=0, tiled=True, +) +save_video(video, "video_Wan2.2-T2V-A14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py new file mode 100644 index 0000000000000000000000000000000000000000..ca968d599e42f66e94bb0e66ca925f45d9514a14 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py @@ -0,0 +1,43 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-TI2V-5B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.2-TI2V-5B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-TI2V-5B", origin_file_pattern="Wan2.2_VAE.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +# Text-to-video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + seed=0, tiled=True, + height=704, width=1248, + num_frames=121, +) +save_video(video, "video_1_Wan2.2-TI2V-5B.mp4", fps=15, quality=5) + +# Image-to-video +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/cat_fightning.jpg"] +) +input_image = Image.open("data/examples/wan/cat_fightning.jpg").resize((1248, 704)) +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + seed=0, tiled=True, + height=704, width=1248, + input_image=input_image, + num_frames=121, +) +save_video(video, "video_2_Wan2.2-TI2V-5B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-VACE-Fun-A14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-VACE-Fun-A14B.py new file mode 100644 index 0000000000000000000000000000000000000000..10566fa1a747be3941614f259857c9bdfe0d37f6 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/Wan2.2-VACE-Fun-A14B.py @@ -0,0 +1,68 @@ +# Without VRAM Management, 80G VRAM is not enough to run this example. +# We recommend to use `examples/wanvideo/model_inference_low_vram/Wan2.2-VACE-Fun-A14B.py`. +# CPU Offload is enabled in this example. +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/depth_video.mp4", "data/examples/wan/cat_fightning.jpg"] +) + +# Depth video -> Video +control_video = VideoData("data/examples/wan/depth_video.mp4", height=480, width=832) +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_video=control_video, + seed=1, tiled=True +) +save_video(video, "video_1_Wan2.2-VACE-Fun-A14B.mp4", fps=15, quality=5) + +# Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video_2_Wan2.2-VACE-Fun-A14B.mp4", fps=15, quality=5) + +# Depth video + Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_video=control_video, + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video_3_Wan2.2-VACE-Fun-A14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/krea-realtime-video.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/krea-realtime-video.py new file mode 100644 index 0000000000000000000000000000000000000000..516b04f5b531ae322d75485feeaee262b63172fd --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference/krea-realtime-video.py @@ -0,0 +1,25 @@ +import torch +from diffsynth.utils.data import save_video +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="krea/krea-realtime-video", origin_file_pattern="krea-realtime-video-14b.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +# Text-to-video +video = pipe( + prompt="a cat sitting on a boat", + num_inference_steps=6, num_frames=81, + seed=0, tiled=True, + cfg_scale=1, + sigma_shift=20, +) +save_video(video, "video_krea-realtime-video.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/LongCat-Video.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/LongCat-Video.py new file mode 100644 index 0000000000000000000000000000000000000000..fa1c4faf3864b605329f7c43331842ed99133f86 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/LongCat-Video.py @@ -0,0 +1,46 @@ +import torch +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="meituan-longcat/LongCat-Video", origin_file_pattern="dit/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +# Text-to-video +video = pipe( + prompt="In a realistic photography style, a white boy around seven or eight years old sits on a park bench, wearing a light blue T-shirt, denim shorts, and white sneakers. He holds an ice cream cone with vanilla and chocolate flavors, and beside him is a medium-sized golden Labrador. Smiling, the boy offers the ice cream to the dog, who eagerly licks it with its tongue. The sun is shining brightly, and the background features a green lawn and several tall trees, creating a warm and loving scene.", + negative_prompt="Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards", + seed=0, tiled=True, num_frames=93, + cfg_scale=2, sigma_shift=1, +) +save_video(video, "video_1_LongCat-Video.mp4", fps=15, quality=5) + +# Video-continuation (The number of frames in `longcat_video` should be 4n+1.) +longcat_video = video[-17:] +video = pipe( + prompt="In a realistic photography style, a white boy around seven or eight years old sits on a park bench, wearing a light blue T-shirt, denim shorts, and white sneakers. He holds an ice cream cone with vanilla and chocolate flavors, and beside him is a medium-sized golden Labrador. Smiling, the boy offers the ice cream to the dog, who eagerly licks it with its tongue. The sun is shining brightly, and the background features a green lawn and several tall trees, creating a warm and loving scene.", + negative_prompt="Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards", + seed=1, tiled=True, num_frames=93, + cfg_scale=2, sigma_shift=1, + longcat_video=longcat_video, +) +save_video(video, "video_2_LongCat-Video.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Video-As-Prompt-Wan2.1-14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Video-As-Prompt-Wan2.1-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..b1632b4797adcdf79fa935b485ea49cf3e4b24d5 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Video-As-Prompt-Wan2.1-14B.py @@ -0,0 +1,62 @@ +import torch +import PIL +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download +from typing import List + + +# This model doesn't support fine-grained VRAM Management due to its special architecture. +# Only CPU Offload is supported. +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cuda", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="ByteDance/Video-As-Prompt-Wan2.1-14B", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +dataset_snapshot_download("DiffSynth-Studio/example_video_dataset", allow_file_pattern="wanvap/*", local_dir="data/example_video_dataset") +ref_video_path = 'data/example_video_dataset/wanvap/vap_ref.mp4' +target_image_path = 'data/example_video_dataset/wanvap/input_image.jpg' + +def select_frames(video_frames, num): + idx = torch.linspace(0, len(video_frames) - 1, num).long().tolist() + return [video_frames[i] for i in idx] + +image = Image.open(target_image_path).convert("RGB") +ref_video = VideoData(ref_video_path, height=480, width=832) +ref_frames = select_frames(ref_video, num=49) + +vap_prompt = "A man stands with his back to the camera on a dirt path overlooking sun-drenched, rolling green tea plantations. He wears a blue and green plaid shirt, dark pants, and white shoes. As he turns to face the camera and spreads his arms, a brief, magical burst of sparkling golden light particles envelops him. Through this shimmer, he seamlessly transforms into a Labubu toy character. His head morphs into the iconic large, furry-eared head of the toy, featuring a wide grin with pointed teeth and red cheek markings. The character retains the man's original plaid shirt and clothing, which now fit its stylized, cartoonish body. The camera remains static throughout the transformation, positioned low among the tea bushes, maintaining a consistent view of the subject and the expansive scenery." +prompt = "A young woman with curly hair, wearing a green hijab and a floral dress, plays a violin in front of a vintage green car on a tree-lined street. She executes a swift counter-clockwise turn to face the camera. During the turn, a brilliant shower of golden, sparkling particles erupts and momentarily obscures her figure. As the particles fade, she is revealed to have seamlessly transformed into a Labubu toy character. This new figure, now with the toy's signature large ears, big eyes, and toothy grin, maintains the original pose and continues playing the violin. The character's clothing—the green hijab, floral dress, and black overcoat—remains identical to the woman's. Throughout this transition, the camera stays static, and the street-side environment remains completely consistent." +negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards" + +video = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + input_image=image, + seed=42, tiled=True, + height=480, width=832, + num_frames=49, + vap_video=ref_frames, + vap_prompt=vap_prompt, + negative_vap_prompt=negative_prompt, +) +save_video(video, "video_Video-As-Prompt-Wan2.1-14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan-Dancer-14B-global.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan-Dancer-14B-global.py new file mode 100644 index 0000000000000000000000000000000000000000..51d2ccea271460265811f18ba973384356d6adac --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan-Dancer-14B-global.py @@ -0,0 +1,59 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="global_model.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) +dataset_snapshot_download( + "DiffSynth-Studio/diffsynth_example_dataset", + local_dir="data/diffsynth_example_dataset", + allow_file_pattern="wanvideo/Wan-Dancer-14B-global/*" +) +# This is a specialized model with the following constraints on its input parameters: +# * The model outputs a sequence of keyframes rather than a video; therefore, `framewise_decoding=True` must be set. +# * When the number of keyframes is $n$, `num_frames` = 4 * (n - 1) + 1. +# * Reducing `height`, `width`, `num_frames`, or `num_inference_steps` may lead to severe artifacts or generation failure. +# * The audio file specified by `wantodance_music_path` must match the video duration, calculated as (`num_frames` / 7.5) seconds. +# * The width and height of `wantodance_reference_image` must be multiples of 16. +# * `wantodance_fps` is configurable, but since the model appears to have been trained exclusively at 7.5 FPS, setting it to other values is not recommended. +# * The first frame of `wantodance_keyframes` is the `wantodance_reference_image`, while all subsequent frames are solid black. +# * `wantodance_keyframes_mask` indicates the positions of valid frames within `wantodance_keyframes`. +wantodance_keyframes = VideoData("data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-global/keyframes.mp4") +wantodance_keyframes = [wantodance_keyframes[i] for i in range(149)] +video = pipe( + prompt="一个人正在跳舞，舞蹈种类是韩舞。帧率是7.5000", + 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", + seed=0, tiled=False, + height=1280, width=720, num_frames=149, + num_inference_steps=48, + wantodance_music_path="data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-global/music.WAV", + wantodance_reference_image=Image.open("data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-global/refimage.jpg"), + wantodance_fps=7.5, + wantodance_keyframes=wantodance_keyframes, + wantodance_keyframes_mask=[1] + [0] * 148, + framewise_decoding=True, +) +save_video(video, "video_Wan-Dancer-14B-global.mp4", fps=7.5, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan-Dancer-14B-local.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan-Dancer-14B-local.py new file mode 100644 index 0000000000000000000000000000000000000000..c60d9fc53433642daaf1b73ee2881b3e9139c249 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan-Dancer-14B-local.py @@ -0,0 +1,63 @@ +import torch, os +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="local_model.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) +dataset_snapshot_download( + "DiffSynth-Studio/diffsynth_example_dataset", + local_dir="data/diffsynth_example_dataset", + allow_file_pattern="wanvideo/Wan-Dancer-14B-local/*" +) +# This is a specialized model with the following constraints on its input parameters: +# * The model renders and outputs video based on a sequence of keyframes; therefore, `wantodance_keyframes` must be provided correctly. +# * If you need to generate a long video, please generate it in segments, and ensure that `wantodance_music_path`, `wantodance_keyframes`, and `wantodance_keyframes_mask` are properly split accordingly. +# * The audio file specified by `wantodance_music_path` must match the video duration, calculated as (`num_frames` / 30) seconds. +# * The width and height of `wantodance_reference_image` must be multiples of 16. +# * `wantodance_fps` is configurable, but since the model appears to have been trained exclusively at 30 FPS, setting it to other values is not recommended. +# * In `wantodance_keyframes`, frames that are not keyframes should be solid black. +# * `wantodance_keyframes_mask` indicates the positions of valid frames within `wantodance_keyframes`. +wantodance_keyframes = VideoData("data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-local/keyframes.mp4") +wantodance_keyframes = [wantodance_keyframes[i] for i in range(149)] +video = pipe( + prompt="一个人正在跳舞，舞蹈种类是古典舞,图像清晰程度高,人物动作平均幅度中等,人物动作最大幅度中等。, 帧率是30fps。", + 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", + seed=0, tiled=True, + height=1280, width=720, num_frames=149, + num_inference_steps=24, + wantodance_music_path="data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-local/music.wav", + wantodance_reference_image=Image.open("data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-local/refimage.jpg"), + wantodance_fps=30, + wantodance_keyframes=wantodance_keyframes, + wantodance_keyframes_mask=[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1], +) +save_video(video, "video_Wan-Dancer-14B-local.mp4", fps=30, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-1.3b-speedcontrol-v1.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-1.3b-speedcontrol-v1.py new file mode 100644 index 0000000000000000000000000000000000000000..3d8f5d18f7911cb817c7a4901f35d47851a9432e --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-1.3b-speedcontrol-v1.py @@ -0,0 +1,45 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ModelConfig(model_id="DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1", origin_file_pattern="model.safetensors", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +# Text-to-video +video = pipe( + prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。", + 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", + seed=1, tiled=True, + motion_bucket_id=0 +) +save_video(video, "video_slow_Wan2.1-1.3b-speedcontrol-v1.mp4", fps=15, quality=5) + +video = pipe( + prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。", + 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", + seed=1, tiled=True, + motion_bucket_id=100 +) +save_video(video, "video_fast_Wan2.1-1.3b-speedcontrol-v1.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-FLF2V-14B-720P.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-FLF2V-14B-720P.py new file mode 100644 index 0000000000000000000000000000000000000000..5af7fb7ca1b4afb20d67963fea5dbedb0934c9f0 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-FLF2V-14B-720P.py @@ -0,0 +1,47 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/first_frame.jpeg", "data/examples/wan/last_frame.jpeg"] +) + +# First and last frame to video +video = pipe( + prompt="写实风格，一个女生手持枯萎的花站在花园中，镜头逐渐拉远，记录下花园的全貌。", + 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", + input_image=Image.open("data/examples/wan/first_frame.jpeg").resize((960, 960)), + end_image=Image.open("data/examples/wan/last_frame.jpeg").resize((960, 960)), + seed=0, tiled=True, + height=960, width=960, num_frames=33, + sigma_shift=16, +) +save_video(video, "video_Wan2.1-FLF2V-14B-720P.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-1.3B-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-1.3B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..500db9f81a75f19bf40eec6f8b4f5b3bb0a90b59 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-1.3B-Control.py @@ -0,0 +1,45 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/control_video.mp4" +) + +# Control video +control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576) +video = pipe( + prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。", + 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", + control_video=control_video, height=832, width=576, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-1.3B-Control.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-1.3B-InP.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-1.3B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..d3533ba60156d0722ba469e88b4127c6f7ea7a06 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-1.3B-InP.py @@ -0,0 +1,47 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# First and last frame to video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + input_image=image, + seed=0, tiled=True + # You can input `end_image=xxx` to control the last frame of the video. + # The model will automatically generate the dynamic content between `input_image` and `end_image`. +) +save_video(video, "video_Wan2.1-Fun-1.3B-InP.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-14B-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-14B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..aaa26f020ee26109fd47ec4de4c814407f3152b7 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-14B-Control.py @@ -0,0 +1,45 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/control_video.mp4" +) + +# Control video +control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576) +video = pipe( + prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。", + 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", + control_video=control_video, height=832, width=576, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-14B-Control.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-14B-InP.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-14B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..c56dbffbeae7ebd5d1a683cd46ca7d7b4266f47a --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-14B-InP.py @@ -0,0 +1,47 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# First and last frame to video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + input_image=image, + seed=0, tiled=True + # You can input `end_image=xxx` to control the last frame of the video. + # The model will automatically generate the dynamic content between `input_image` and `end_image`. +) +save_video(video, "video_Wan2.1-Fun-14B-InP.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..658c0ebc58b5d06bd59b2e8e753f067f6a124c99 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py @@ -0,0 +1,55 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +input_image = Image.open("data/examples/wan/input_image.jpg") + +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + seed=0, tiled=True, + input_image=input_image, + camera_control_direction="Left", camera_control_speed=0.01, +) +save_video(video, "video_left_Wan2.1-Fun-V1.1-1.3B-Control-Camera.mp4", fps=15, quality=5) + +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + seed=0, tiled=True, + input_image=input_image, + camera_control_direction="Up", camera_control_speed=0.01, +) +save_video(video, "video_up_Wan2.1-Fun-V1.1-1.3B-Control-Camera.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-V1.1-1.3B-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-V1.1-1.3B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..20eb6871236241fada69697a61d2f1e1325c31b3 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-V1.1-1.3B-Control.py @@ -0,0 +1,47 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/control_video.mp4", "data/examples/wan/reference_image_girl.png"] +) + +# Control video +control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576) +reference_image = Image.open("data/examples/wan/reference_image_girl.png").resize((576, 832)) +video = pipe( + prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。", + 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", + control_video=control_video, reference_image=reference_image, + height=832, width=576, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-1.3B-Control.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-V1.1-1.3B-InP.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-V1.1-1.3B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..fc95d4c5bf6b46bf4c52c7ed5f17a63a6c88819f --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-V1.1-1.3B-InP.py @@ -0,0 +1,47 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# First and last frame to video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + input_image=image, + seed=0, tiled=True + # You can input `end_image=xxx` to control the last frame of the video. + # The model will automatically generate the dynamic content between `input_image` and `end_image`. +) +save_video(video, "video_Wan2.1-Fun-V1.1-1.3B-InP.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-V1.1-14B-Control-Camera.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-V1.1-14B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..37434ba16223612f9eb961528d990b023e52be2f --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-V1.1-14B-Control-Camera.py @@ -0,0 +1,55 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +input_image = Image.open("data/examples/wan/input_image.jpg") + +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + seed=0, tiled=True, + input_image=input_image, + camera_control_direction="Left", camera_control_speed=0.01, +) +save_video(video, "video_left_Wan2.1-Fun-V1.1-14B-Control-Camera.mp4", fps=15, quality=5) + +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + seed=0, tiled=True, + input_image=input_image, + camera_control_direction="Up", camera_control_speed=0.01, +) +save_video(video, "video_up_Wan2.1-Fun-V1.1-14B-Control-Camera.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-V1.1-14B-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-V1.1-14B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..bcb66f3e34e9a2ae59a3b4ed662fccb79a9348ff --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-V1.1-14B-Control.py @@ -0,0 +1,47 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/control_video.mp4", "data/examples/wan/reference_image_girl.png"] +) + +# Control video +control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576) +reference_image = Image.open("data/examples/wan/reference_image_girl.png").resize((576, 832)) +video = pipe( + prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。", + 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", + control_video=control_video, reference_image=reference_image, + height=832, width=576, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-14B-Control.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-V1.1-14B-InP.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-V1.1-14B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..d9ae5880fed7d7e065affa6f790b2e9baf9f4e73 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-Fun-V1.1-14B-InP.py @@ -0,0 +1,47 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# First and last frame to video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + input_image=image, + seed=0, tiled=True + # You can input `end_image=xxx` to control the last frame of the video. + # The model will automatically generate the dynamic content between `input_image` and `end_image`. +) +save_video(video, "video_Wan2.1-Fun-V1.1-14B-InP.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-I2V-14B-480P.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-I2V-14B-480P.py new file mode 100644 index 0000000000000000000000000000000000000000..b14412c8e4839092ed81a868ba89a85a0a756ff8 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-I2V-14B-480P.py @@ -0,0 +1,45 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# Image-to-video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + input_image=image, + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-I2V-14B-480P.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-I2V-14B-720P.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-I2V-14B-720P.py new file mode 100644 index 0000000000000000000000000000000000000000..7ec01b4e3b752dfdca1f7288e788e53cdd3b1d45 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-I2V-14B-720P.py @@ -0,0 +1,46 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# Image-to-video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + input_image=image, + seed=0, tiled=True, + height=720, width=1280, +) +save_video(video, "video_Wan2.1-I2V-14B-720P.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-T2V-1.3B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-T2V-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..6f90d68aec798fd7f0d45ea06ba618e77f1cf722 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-T2V-1.3B.py @@ -0,0 +1,45 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +# Text-to-video +video = pipe( + prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。", + 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", + seed=0, tiled=True, +) +save_video(video, "video_1_Wan2.1-T2V-1.3B.mp4", fps=15, quality=5) + +# Video-to-video +video = VideoData("video_1_Wan2.1-T2V-1.3B.mp4", height=480, width=832) +video = pipe( + prompt="纪实摄影风格画面，一只活泼的小狗戴着黑色墨镜在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，戴着黑色墨镜，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。", + 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", + input_video=video, denoising_strength=0.7, + seed=1, tiled=True +) +save_video(video, "video_2_Wan2.1-T2V-1.3B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-T2V-14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-T2V-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..68555b56d051079ed1737b6cbb6236ed1f037533 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-T2V-14B.py @@ -0,0 +1,35 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +# Text-to-video +video = pipe( + prompt="一名宇航员身穿太空服，面朝镜头骑着一匹机械马在火星表面驰骋。红色的荒凉地表延伸至远方，点缀着巨大的陨石坑和奇特的岩石结构。机械马的步伐稳健，扬起微弱的尘埃，展现出未来科技与原始探索的完美结合。宇航员手持操控装置，目光坚定，仿佛正在开辟人类的新疆域。背景是深邃的宇宙和蔚蓝的地球，画面既科幻又充满希望，让人不禁畅想未来的星际生活。", + 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", + seed=0, tiled=True, +) +save_video(video, "video_Wan2.1-T2V-14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-VACE-1.3B-Preview.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-VACE-1.3B-Preview.py new file mode 100644 index 0000000000000000000000000000000000000000..5f118198cfbfa48d1637bf885052286e814a2426 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-VACE-1.3B-Preview.py @@ -0,0 +1,63 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/depth_video.mp4", "data/examples/wan/cat_fightning.jpg"] +) + +# Depth video -> Video +control_video = VideoData("data/examples/wan/depth_video.mp4", height=480, width=832) +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_video=control_video, + seed=1, tiled=True +) +save_video(video, "video_1_Wan2.1-VACE-1.3B-Preview.mp4", fps=15, quality=5) + +# Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video_2_Wan2.1-VACE-1.3B-Preview.mp4", fps=15, quality=5) + +# Depth video + Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_video=control_video, + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video_3_Wan2.1-VACE-1.3B-Preview.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-VACE-1.3B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-VACE-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..60bd8c0e551db14e2e6d08ab80bf79d03315e5bd --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-VACE-1.3B.py @@ -0,0 +1,64 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/depth_video.mp4", "data/examples/wan/cat_fightning.jpg"] +) + +# Depth video -> Video +control_video = VideoData("data/examples/wan/depth_video.mp4", height=480, width=832) +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_video=control_video, + seed=1, tiled=True +) +save_video(video, "video_1_Wan2.1-VACE-1.3B.mp4", fps=15, quality=5) + +# Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video_2_Wan2.1-VACE-1.3B.mp4", fps=15, quality=5) + +# Depth video + Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_video=control_video, + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video_3_Wan2.1-VACE-1.3B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-VACE-14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-VACE-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..7aa795e36378273c1fc56ecc7a996cfc1923ff26 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.1-VACE-14B.py @@ -0,0 +1,65 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + + + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/depth_video.mp4", "data/examples/wan/cat_fightning.jpg"] +) + +# Depth video -> Video +control_video = VideoData("data/examples/wan/depth_video.mp4", height=480, width=832) +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_video=control_video, + seed=1, tiled=True +) +save_video(video, "video_1_Wan2.1-VACE-14B.mp4", fps=15, quality=5) + +# Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video_2_Wan2.1-VACE-14B.mp4", fps=15, quality=5) + +# Depth video + Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_video=control_video, + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video_3_Wan2.1-VACE-14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-Animate-14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-Animate-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..180482c14e489a87324a1337adcb3bd7510053c7 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-Animate-14B.py @@ -0,0 +1,74 @@ +import torch +from PIL import Image +from diffsynth.core import load_state_dict +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download, snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern="data/examples/wan/animate/*", +) + +# Animate +input_image = Image.open("data/examples/wan/animate/animate_input_image.png") +animate_pose_video = VideoData("data/examples/wan/animate/animate_pose_video.mp4").raw_data()[:81-4] +animate_face_video = VideoData("data/examples/wan/animate/animate_face_video.mp4").raw_data()[:81-4] +video = pipe( + prompt="视频中的人在做动作", + seed=0, tiled=True, + input_image=input_image, + animate_pose_video=animate_pose_video, + animate_face_video=animate_face_video, + num_frames=81, height=720, width=1280, + num_inference_steps=20, cfg_scale=1, +) +save_video(video, "video_1_Wan2.2-Animate-14B.mp4", fps=15, quality=5) + +# Replace +snapshot_download("Wan-AI/Wan2.2-Animate-14B", allow_file_pattern="relighting_lora.ckpt", local_dir="models/Wan-AI/Wan2.2-Animate-14B") +lora_state_dict = load_state_dict("models/Wan-AI/Wan2.2-Animate-14B/relighting_lora.ckpt", torch_dtype=torch.bfloat16, device="cuda")["state_dict"] +lora_state_dict = {i: lora_state_dict[i].to(torch.bfloat16) for i in lora_state_dict} +pipe.load_lora(pipe.dit, state_dict=lora_state_dict) +input_image = Image.open("data/examples/wan/animate/replace_input_image.png") +animate_pose_video = VideoData("data/examples/wan/animate/replace_pose_video.mp4").raw_data()[:81-4] +animate_face_video = VideoData("data/examples/wan/animate/replace_face_video.mp4").raw_data()[:81-4] +animate_inpaint_video = VideoData("data/examples/wan/animate/replace_inpaint_video.mp4").raw_data()[:81-4] +animate_mask_video = VideoData("data/examples/wan/animate/replace_mask_video.mp4").raw_data()[:81-4] +video = pipe( + prompt="视频中的人在做动作", + seed=0, tiled=True, + input_image=input_image, + animate_pose_video=animate_pose_video, + animate_face_video=animate_face_video, + animate_inpaint_video=animate_inpaint_video, + animate_mask_video=animate_mask_video, + num_frames=81, height=720, width=1280, + num_inference_steps=20, cfg_scale=1, +) +save_video(video, "video_2_Wan2.2-Animate-14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-Fun-A14B-Control-Camera.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-Fun-A14B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..760bd7e271739af44127e582cc862f2c39902f2c --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-Fun-A14B-Control-Camera.py @@ -0,0 +1,55 @@ +import torch +from diffsynth.utils.data import save_video,VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from PIL import Image +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +input_image = Image.open("data/examples/wan/input_image.jpg") + +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + seed=0, tiled=True, + input_image=input_image, + camera_control_direction="Left", camera_control_speed=0.01, +) +save_video(video, "video_left_Wan2.2-Fun-A14B-Control-Camera.mp4", fps=15, quality=5) + +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + seed=0, tiled=True, + input_image=input_image, + camera_control_direction="Up", camera_control_speed=0.01, +) +save_video(video, "video_up_Wan2.2-Fun-A14B-Control-Camera.mp4", fps=15, quality=5) \ No newline at end of file diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-Fun-A14B-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-Fun-A14B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..df92f7d2306f8f0ce2c26453e8c64f9f73581999 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-Fun-A14B-Control.py @@ -0,0 +1,46 @@ +import torch +from diffsynth.utils.data import save_video,VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from PIL import Image +from modelscope import dataset_snapshot_download + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/control_video.mp4", "data/examples/wan/reference_image_girl.png"] +) + +# Control video +control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576) +reference_image = Image.open("data/examples/wan/reference_image_girl.png").resize((576, 832)) +video = pipe( + prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。", + 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", + control_video=control_video, reference_image=reference_image, + height=832, width=576, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.2-Fun-A14B-Control.mp4", fps=15, quality=5) \ No newline at end of file diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-Fun-A14B-InP.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-Fun-A14B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..34f8cfaa99e76e3c784c8f14c4d9dec65d0a8355 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-Fun-A14B-InP.py @@ -0,0 +1,46 @@ +import torch +from diffsynth.utils.data import save_video +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from PIL import Image +from modelscope import dataset_snapshot_download + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# First and last frame to video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + 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", + input_image=image, + seed=0, tiled=True, + # You can input `end_image=xxx` to control the last frame of the video. + # The model will automatically generate the dynamic content between `input_image` and `end_image`. +) +save_video(video, "video_Wan2.2-Fun-A14B-InP.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-I2V-A14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-I2V-A14B.py new file mode 100644 index 0000000000000000000000000000000000000000..2bc8a6715e5cf03840f2b83feb59b708eba2c5b9 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-I2V-A14B.py @@ -0,0 +1,44 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/cat_fightning.jpg"] +) +input_image = Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)) + +video = pipe( + prompt="Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.", + 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", + seed=0, tiled=True, + input_image=input_image, + switch_DiT_boundary=0.9, +) +save_video(video, "video_Wan2.2-I2V-A14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-S2V-14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-S2V-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..14be8c919568978b0d3303099f33a2c6c627c19b --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-S2V-14B.py @@ -0,0 +1,84 @@ +# This script can generate a single video clip. +# If you need generate long videos, please refer to `Wan2.2-S2V-14B_multi_clips.py`. +import torch +from PIL import Image +import librosa +from diffsynth.utils.data import VideoData, save_video_with_audio +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/model.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + audio_processor_config=ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_video_dataset", + local_dir="./data/example_video_dataset", + allow_file_pattern=f"wans2v/*" +) + +num_frames = 81 # 4n+1 +height = 448 +width = 832 + +prompt = "a person is singing" +negative_prompt = "画面模糊，最差质量，画面模糊，细节模糊不清，情绪激动剧烈，手快速抖动，字幕，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" +input_image = Image.open("data/example_video_dataset/wans2v/pose.png").convert("RGB").resize((width, height)) +# s2v audio input, recommend 16kHz sampling rate +audio_path = 'data/example_video_dataset/wans2v/sing.MP3' +input_audio, sample_rate = librosa.load(audio_path, sr=16000) + +# Speech-to-video +video = pipe( + prompt=prompt, + input_image=input_image, + negative_prompt=negative_prompt, + seed=0, + num_frames=num_frames, + height=height, + width=width, + audio_sample_rate=sample_rate, + input_audio=input_audio, + num_inference_steps=40, +) +save_video_with_audio(video[1:], "video_1_Wan2.2-S2V-14B.mp4", audio_path, fps=16, quality=5) + +# s2v will use the first (num_frames) frames as reference. height and width must be the same as input_image. And fps should be 16, the same as output video fps. +pose_video_path = 'data/example_video_dataset/wans2v/pose.mp4' +pose_video = VideoData(pose_video_path, height=height, width=width) + +# Speech-to-video with pose +video = pipe( + prompt=prompt, + input_image=input_image, + negative_prompt=negative_prompt, + seed=0, + num_frames=num_frames, + height=height, + width=width, + audio_sample_rate=sample_rate, + input_audio=input_audio, + s2v_pose_video=pose_video, + num_inference_steps=40, +) +save_video_with_audio(video[1:], "video_2_Wan2.2-S2V-14B.mp4", audio_path, fps=16, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-S2V-14B_multi_clips.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-S2V-14B_multi_clips.py new file mode 100644 index 0000000000000000000000000000000000000000..e57d65e4dec95e5eec9b8fc2148a25743e9690fc --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-S2V-14B_multi_clips.py @@ -0,0 +1,133 @@ +import torch +from PIL import Image +import librosa +from diffsynth.utils.data import VideoData, save_video_with_audio +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig, WanVideoUnit_S2V +from modelscope import dataset_snapshot_download + + +def speech_to_video( + prompt, + input_image, + audio_path, + negative_prompt="", + num_clip=None, + audio_sample_rate=16000, + pose_video_path=None, + infer_frames=80, + height=448, + width=832, + num_inference_steps=40, + fps=16, # recommend fixing fps as 16 for s2v + motion_frames=73, # hyperparameter of wan2.2-s2v + save_path=None, +): + # s2v audio input, recommend 16kHz sampling rate + input_audio, sample_rate = librosa.load(audio_path, sr=audio_sample_rate) + # s2v will use the first (num_frames) frames as reference. height and width must be the same as input_image. And fps should be 16, the same as output video fps. + pose_video = VideoData(pose_video_path, height=height, width=width) if pose_video_path is not None else None + + with torch.no_grad(): + audio_embeds, pose_latents, num_repeat = WanVideoUnit_S2V.pre_calculate_audio_pose( + pipe=pipe, + input_audio=input_audio, + audio_sample_rate=sample_rate, + s2v_pose_video=pose_video, + num_frames=infer_frames + 1, + height=height, + width=width, + fps=fps, + ) + num_repeat = min(num_repeat, num_clip) if num_clip is not None else num_repeat + print(f"Generating {num_repeat} video clips...") + motion_video = None + video = [] + for r in range(num_repeat): + s2v_pose_latents = pose_latents[r] if pose_latents is not None else None + current_clip_tensor = pipe( + prompt=prompt, + input_image=input_image, + negative_prompt=negative_prompt, + seed=0, + num_frames=infer_frames + 1, + height=height, + width=width, + audio_embeds=audio_embeds[r], + s2v_pose_latents=s2v_pose_latents, + motion_video=motion_video, + num_inference_steps=num_inference_steps, + output_type="floatpoint", + ) + current_clip_tensor = current_clip_tensor[:,:,-infer_frames:,:,:] + if r == 0: + current_clip_tensor = current_clip_tensor[:,:,3:,:,:] + overlap_frames_num = min(motion_frames, current_clip_tensor.shape[2]) + motion_video = current_clip_tensor[:,:,-overlap_frames_num:,:,:].clone() + else: + overlap_frames_num = min(motion_frames, current_clip_tensor.shape[2]) + motion_video = torch.cat((motion_video[:,:,overlap_frames_num:,:,:], current_clip_tensor[:,:,-overlap_frames_num:,:,:]), dim=2) + current_clip_quantized = pipe.vae_output_to_video(current_clip_tensor) + video.extend(current_clip_quantized) + save_video_with_audio(video, save_path, audio_path, fps=16, quality=5) + print(f"processed the {r+1}th clip of total {num_repeat} clips.") + return video + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/model.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + audio_processor_config=ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_video_dataset", + local_dir="./data/example_video_dataset", + allow_file_pattern=f"wans2v/*", +) + +infer_frames = 80 # 4n +height = 448 +width = 832 + +prompt = "a person is singing" +negative_prompt = "画面模糊，最差质量，画面模糊，细节模糊不清，情绪激动剧烈，手快速抖动，字幕，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" +input_image = Image.open("data/example_video_dataset/wans2v/pose.png").convert("RGB").resize((width, height)) + +video_with_audio = speech_to_video( + prompt=prompt, + input_image=input_image, + audio_path='data/example_video_dataset/wans2v/sing.MP3', + negative_prompt=negative_prompt, + pose_video_path='data/example_video_dataset/wans2v/pose.mp4', + save_path="video_full_Wan2.2-S2V-14B.mp4", + infer_frames=infer_frames, + height=height, + width=width, +) +# num_clip means generating only the first n clips with n * infer_frames frames. +video_with_audio_pose = speech_to_video( + prompt=prompt, + input_image=input_image, + audio_path='data/example_video_dataset/wans2v/sing.MP3', + negative_prompt=negative_prompt, + pose_video_path='data/example_video_dataset/wans2v/pose.mp4', + save_path="video_clip_2_Wan2.2-S2V-14B.mp4", + num_clip=2 +) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-T2V-A14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-T2V-A14B.py new file mode 100644 index 0000000000000000000000000000000000000000..bf5155c71b160a7457965a53a5aee69e6bb6075c --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-T2V-A14B.py @@ -0,0 +1,35 @@ +import torch +from diffsynth.utils.data import save_video +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +# Text-to-video +video = pipe( + prompt="Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.", + 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", + seed=0, tiled=True, +) +save_video(video, "video_Wan2.2-T2V-A14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-TI2V-5B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-TI2V-5B.py new file mode 100644 index 0000000000000000000000000000000000000000..34cbfae93d6980d6f162b4d6ec93021c4e277120 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-TI2V-5B.py @@ -0,0 +1,54 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-TI2V-5B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.2-TI2V-5B", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.2-TI2V-5B", origin_file_pattern="Wan2.2_VAE.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +# Text-to-video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + seed=0, tiled=True, + height=704, width=1248, + num_frames=121, +) +save_video(video, "video_1_Wan2.2-TI2V-5B.mp4", fps=15, quality=5) + +# Image-to-video +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/cat_fightning.jpg"] +) +input_image = Image.open("data/examples/wan/cat_fightning.jpg").resize((1248, 704)) +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + seed=0, tiled=True, + height=704, width=1248, + input_image=input_image, + num_frames=121, +) +save_video(video, "video_2_Wan2.2-TI2V-5B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-VACE-Fun-A14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-VACE-Fun-A14B.py new file mode 100644 index 0000000000000000000000000000000000000000..3474b01d9236b9605acc8c88f822ac71e91cb7e8 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/Wan2.2-VACE-Fun-A14B.py @@ -0,0 +1,65 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", **vram_config), + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/depth_video.mp4", "data/examples/wan/cat_fightning.jpg"] +) + +# Depth video -> Video +control_video = VideoData("data/examples/wan/depth_video.mp4", height=480, width=832) +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_video=control_video, + seed=1, tiled=True +) +save_video(video, "video_1_Wan2.2-VACE-Fun-A14B.mp4", fps=15, quality=5) + +# Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video_2_Wan2.2-VACE-Fun-A14B.mp4", fps=15, quality=5) + +# Depth video + Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + 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", + vace_video=control_video, + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video_3_Wan2.2-VACE-Fun-A14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/krea-realtime-video.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/krea-realtime-video.py new file mode 100644 index 0000000000000000000000000000000000000000..ffc7a3f1e667a27fcf902cdbed942922dfc54281 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_inference_low_vram/krea-realtime-video.py @@ -0,0 +1,36 @@ +import torch +from diffsynth.utils.data import save_video +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +vram_config = { + "offload_dtype": "disk", + "offload_device": "disk", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="krea/krea-realtime-video", origin_file_pattern="krea-realtime-video-14b.safetensors", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), + vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2, +) + +# Text-to-video +video = pipe( + prompt="a cat sitting on a boat", + num_inference_steps=6, num_frames=81, + seed=0, tiled=True, + cfg_scale=1, + sigma_shift=20, +) +save_video(video, "video_krea-realtime-video.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/LongCat-Video.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/LongCat-Video.sh new file mode 100644 index 0000000000000000000000000000000000000000..70526e36250c128458440cb1744dc30a6787089a --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/LongCat-Video.sh @@ -0,0 +1,14 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/LongCat-Video/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/LongCat-Video \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/LongCat-Video/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "meituan-longcat/LongCat-Video:dit/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LongCat-Video_full" \ + --trainable_models "dit" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Video-As-Prompt-Wan2.1-14B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Video-As-Prompt-Wan2.1-14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..b61fff210d208656da8bbd55a59ddbad83a07c8a --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Video-As-Prompt-Wan2.1-14B.sh @@ -0,0 +1,18 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Video-As-Prompt-Wan2.1-14B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Video-As-Prompt-Wan2.1-14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Video-As-Prompt-Wan2.1-14B/metadata.csv \ + --data_file_keys "video,vap_video" \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "ByteDance/Video-As-Prompt-Wan2.1-14B:transformer/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-720P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-720P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-720P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.vap." \ + --output_path "./models/train/Video-As-Prompt-Wan2.1-14B_full" \ + --trainable_models "vap" \ + --extra_inputs "vap_video,input_image" \ + --use_gradient_checkpointing_offload diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan-Dancer-14B-global.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan-Dancer-14B-global.sh new file mode 100644 index 0000000000000000000000000000000000000000..f64e26cf21882a6f850c5504daf4f16b4707fb00 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan-Dancer-14B-global.sh @@ -0,0 +1,20 @@ +# 8*H200 required +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan-Dancer-14B-global/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-global \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-global/metadata.json \ + --data_file_keys "video,wantodance_reference_image,wantodance_keyframes,wantodance_music_path" \ + --height 1280 \ + --width 720 \ + --num_frames 149 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan-Dancer-14B:global_model.safetensors,Wan-AI/Wan-Dancer-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan-Dancer-14B:Wan2.1_VAE.pth,Wan-AI/Wan-Dancer-14B:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan-Dancer-14B-global_full" \ + --trainable_models "dit" \ + --extra_inputs "wantodance_music_path,wantodance_reference_image,wantodance_fps,wantodance_keyframes,wantodance_keyframes_mask,framewise_decoding" \ + --use_gradient_checkpointing_offload \ + --framewise_decoding diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan-Dancer-14B-local.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan-Dancer-14B-local.sh new file mode 100644 index 0000000000000000000000000000000000000000..8e642a078ce69ad9ec1aef1c246507a7d894d2f1 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan-Dancer-14B-local.sh @@ -0,0 +1,19 @@ +# 8*H200 required +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan-Dancer-14B-local/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-local \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-local/metadata.json \ + --data_file_keys "video,wantodance_reference_image,wantodance_keyframes,wantodance_music_path" \ + --height 1280 \ + --width 720 \ + --num_frames 149 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan-Dancer-14B:local_model.safetensors,Wan-AI/Wan-Dancer-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan-Dancer-14B:Wan2.1_VAE.pth,Wan-AI/Wan-Dancer-14B:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan-Dancer-14B-local_full" \ + --trainable_models "dit" \ + --extra_inputs "wantodance_music_path,wantodance_reference_image,wantodance_fps,wantodance_keyframes,wantodance_keyframes_mask" \ + --use_gradient_checkpointing_offload diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh new file mode 100644 index 0000000000000000000000000000000000000000..dd20a41040adea358a3a4f54826f07acc4d31fb3 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh @@ -0,0 +1,15 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-1.3b-speedcontrol-v1/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-1.3b-speedcontrol-v1 \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-1.3b-speedcontrol-v1/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-1.3B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-1.3B:Wan2.1_VAE.pth,DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1:model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.motion_controller." \ + --output_path "./models/train/Wan2.1-1.3b-speedcontrol-v1_full" \ + --trainable_models "motion_controller" \ + --extra_inputs "motion_bucket_id" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh new file mode 100644 index 0000000000000000000000000000000000000000..927c4b281c6ed3e8dcd37b96ab6091d3a07bfaaa --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh @@ -0,0 +1,16 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-FLF2V-14B-720P/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-FLF2V-14B-720P \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-FLF2V-14B-720P/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-FLF2V-14B-720P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-FLF2V-14B-720P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-FLF2V-14B-720P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-FLF2V-14B-720P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-FLF2V-14B-720P_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,end_image" \ + --initialize_model_on_cpu diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..b1d8aa015867f38b9fedb6d77b10065978365953 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh @@ -0,0 +1,16 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-Fun-1.3B-Control/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-1.3B-Control \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-1.3B-Control/metadata.csv \ + --data_file_keys "video,control_video" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-1.3B-Control:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-1.3B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-1.3B-Control:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-1.3B-Control:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-1.3B-Control_full" \ + --trainable_models "dit" \ + --extra_inputs "control_video" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh new file mode 100644 index 0000000000000000000000000000000000000000..01849ed585935108fc4b085e160b2e23c2bd3dec --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh @@ -0,0 +1,15 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-Fun-1.3B-InP/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-1.3B-InP \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-1.3B-InP/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-1.3B-InP:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-1.3B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-1.3B-InP:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-1.3B-InP:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-1.3B-InP_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,end_image" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..d4b9e6d3eaad212844dd814961bef7087e8355c7 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh @@ -0,0 +1,16 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-Fun-14B-Control/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-14B-Control \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-14B-Control/metadata.csv \ + --data_file_keys "video,control_video" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-14B-Control:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-14B-Control:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-14B-Control:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-14B-Control_full" \ + --trainable_models "dit" \ + --extra_inputs "control_video" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh new file mode 100644 index 0000000000000000000000000000000000000000..178d3467d9b69f30522855399ced620149bdfd9a --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh @@ -0,0 +1,15 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-Fun-14B-InP/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-14B-InP \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-14B-InP/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-14B-InP:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-14B-InP:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-14B-InP:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-14B-InP_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,end_image" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh new file mode 100644 index 0000000000000000000000000000000000000000..b01f6641c0bfac965cb57f9587b3ab0bf93ec51f --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh @@ -0,0 +1,15 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-Fun-V1.1-1.3B-Control-Camera/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-1.3B-Control-Camera \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-1.3B-Control-Camera/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-1.3B-Control-Camera_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,camera_control_direction,camera_control_speed" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..f4aff51531ab7c025aa95133b2c5f5724d025822 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh @@ -0,0 +1,16 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-Fun-V1.1-1.3B-Control/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-1.3B-Control \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-1.3B-Control/metadata.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-1.3B-Control:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-1.3B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-1.3B-Control:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-1.3B-Control:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-1.3B-Control_full" \ + --trainable_models "dit" \ + --extra_inputs "control_video,reference_image" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh new file mode 100644 index 0000000000000000000000000000000000000000..adc0796c90bed92108a3bcb4f4478272fc97464b --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh @@ -0,0 +1,15 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-Fun-V1.1-1.3B-InP/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-1.3B-InP \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-1.3B-InP/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-1.3B-InP:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-1.3B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-1.3B-InP:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-1.3B-InP:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-1.3B-InP_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,end_image" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh new file mode 100644 index 0000000000000000000000000000000000000000..71e4c011682b972f070e824a25104d88e8f0b2dd --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh @@ -0,0 +1,15 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-Fun-V1.1-14B-Control-Camera/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-14B-Control-Camera \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-14B-Control-Camera/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-14B-Control-Camera:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-14B-Control-Camera:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-14B-Control-Camera:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-14B-Control-Camera:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-14B-Control-Camera_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,camera_control_direction,camera_control_speed" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..45e9e83de08e3dfff4a21fd66211d7ed26d56e52 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh @@ -0,0 +1,16 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-Fun-V1.1-14B-Control/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-14B-Control \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-14B-Control/metadata.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-14B-Control:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-14B-Control:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-14B-Control:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-14B-Control_full" \ + --trainable_models "dit" \ + --extra_inputs "control_video,reference_image" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh new file mode 100644 index 0000000000000000000000000000000000000000..91f0355ca7c4d7eb61c0d9c89e5dbd37ecfbdfdf --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh @@ -0,0 +1,15 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-Fun-V1.1-14B-InP/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-14B-InP \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-14B-InP/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-14B-InP:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-14B-InP:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-14B-InP:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-14B-InP_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,end_image" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh new file mode 100644 index 0000000000000000000000000000000000000000..e6ef563547eff822ce806cddc8cf8417bb21d804 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh @@ -0,0 +1,16 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-I2V-14B-480P/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-I2V-14B-480P \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-I2V-14B-480P/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-I2V-14B-480P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-480P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-480P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-480P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-I2V-14B-480P_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image" \ + --initialize_model_on_cpu diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh new file mode 100644 index 0000000000000000000000000000000000000000..1efff9409d3186b02c63b058b2f78863145d8efa --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh @@ -0,0 +1,18 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-I2V-14B-720P/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-I2V-14B-720P \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-I2V-14B-720P/metadata.csv \ + --height 720 \ + --width 1280 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-I2V-14B-720P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-720P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-720P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-720P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-I2V-14B-720P_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image" \ + --use_gradient_checkpointing_offload \ + --initialize_model_on_cpu diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh new file mode 100644 index 0000000000000000000000000000000000000000..91b75392140a442bcf8ac4798ca4d2c0509aeaee --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh @@ -0,0 +1,14 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-T2V-1.3B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-T2V-1.3B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-T2V-1.3B/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-1.3B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-1.3B:Wan2.1_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-T2V-1.3B_full" \ + --trainable_models "dit" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..c108f6ce348c5a5bbee28aca659bd4c3a2fbd19e --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh @@ -0,0 +1,14 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-T2V-14B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-T2V-14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-T2V-14B/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-T2V-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-T2V-14B_full" \ + --trainable_models "dit" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh new file mode 100644 index 0000000000000000000000000000000000000000..1554a812f603030b9c72a6a6e28add2f466ebee2 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh @@ -0,0 +1,19 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-VACE-1.3B-Preview/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-VACE-1.3B-Preview \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-VACE-1.3B-Preview/metadata.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "iic/VACE-Wan2.1-1.3B-Preview:diffusion_pytorch_model*.safetensors,iic/VACE-Wan2.1-1.3B-Preview:models_t5_umt5-xxl-enc-bf16.pth,iic/VACE-Wan2.1-1.3B-Preview:Wan2.1_VAE.pth" \ + --learning_rate 5e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.1-VACE-1.3B-Preview_full" \ + --trainable_models "vace" \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload +# The learning rate is kept consistent with the settings in the original paper diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh new file mode 100644 index 0000000000000000000000000000000000000000..5ac3cc7ca78f4fe3e08060cb35a3806ea561b9f6 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh @@ -0,0 +1,19 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-VACE-1.3B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-VACE-1.3B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-VACE-1.3B/metadata.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-VACE-1.3B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-VACE-1.3B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-VACE-1.3B:Wan2.1_VAE.pth" \ + --learning_rate 5e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.1-VACE-1.3B_full" \ + --trainable_models "vace" \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload +# The learning rate is kept consistent with the settings in the original paper diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..043ede5939dedce1898422931a5ac0547a0ee8c6 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh @@ -0,0 +1,19 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-VACE-14B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-VACE-14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-VACE-14B/metadata.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --num_frames 17 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-VACE-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-VACE-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-VACE-14B:Wan2.1_VAE.pth" \ + --learning_rate 5e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.1-VACE-14B_full" \ + --trainable_models "vace" \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload +# The learning rate is kept consistent with the settings in the original paper diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-Animate-14B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-Animate-14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..95474e75400bab8ec59e939c0d4f9e442adf52b6 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-Animate-14B.sh @@ -0,0 +1,18 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-Animate-14B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Animate-14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Animate-14B/metadata.csv \ + --data_file_keys "video,animate_pose_video,animate_face_video" \ + --height 480 \ + --width 832 \ + --num_frames 81 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-Animate-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-Animate-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-Animate-14B:Wan2.1_VAE.pth,Wan-AI/Wan2.2-Animate-14B:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.animate_adapter." \ + --output_path "./models/train/Wan2.2-Animate-14B_full" \ + --trainable_models "animate_adapter" \ + --extra_inputs "input_image,animate_pose_video,animate_face_video" \ + --use_gradient_checkpointing_offload diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh new file mode 100644 index 0000000000000000000000000000000000000000..eaa6d27533177238a2727abf18d87473b8f7f6c3 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh @@ -0,0 +1,37 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-Fun-A14B-Control-Camera/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-Control-Camera \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-Control-Camera/metadata.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control-Camera:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control-Camera:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control-Camera:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-Control-Camera_high_noise_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,camera_control_direction,camera_control_speed" \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [900, 1000] + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-Control-Camera \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-Control-Camera/metadata.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control-Camera:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control-Camera:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control-Camera:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-Control-Camera_low_noise_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,camera_control_direction,camera_control_speed" \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 +# boundary corresponds to timesteps [0, 900] diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..ff836b34f32149d78a71736587a233dca0cdd121 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh @@ -0,0 +1,37 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-Fun-A14B-Control/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-Control \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-Control/metadata.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-Control_high_noise_full" \ + --trainable_models "dit" \ + --extra_inputs "control_video,reference_image" \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [900, 1000] + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-Control \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-Control/metadata.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-Control_low_noise_full" \ + --trainable_models "dit" \ + --extra_inputs "control_video,reference_image" \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 +# boundary corresponds to timesteps [0, 900] diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh new file mode 100644 index 0000000000000000000000000000000000000000..b88b6fd7622a7718f7d45655c4e2b86354a4a8e8 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh @@ -0,0 +1,35 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-Fun-A14B-InP/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-InP \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-InP/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-InP:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-InP:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-InP_high_noise_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,end_image" \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [900, 1000] + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-InP \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-InP/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-InP:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-InP:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-InP_low_noise_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,end_image" \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 +# boundary corresponds to timesteps [0, 900] diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..872cf734c67f5c8bd286f4f789b8b5db89b83ee4 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh @@ -0,0 +1,39 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-I2V-A14B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-I2V-A14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-I2V-A14B/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-I2V-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-I2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-I2V-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-I2V-A14B_high_noise_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image" \ + --use_gradient_checkpointing_offload \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [900, 1000] + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-I2V-A14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-I2V-A14B/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-I2V-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-I2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-I2V-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-I2V-A14B_low_noise_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image" \ + --use_gradient_checkpointing_offload \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 +# boundary corresponds to timesteps [0, 900) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-S2V-14B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-S2V-14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..d7be0f8e7b825ab0db24efdf9e0b93fd4603c012 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-S2V-14B.sh @@ -0,0 +1,19 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-S2V-14B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-S2V-14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-S2V-14B/metadata.csv \ + --data_file_keys "video,input_audio,s2v_pose_video" \ + --height 448 \ + --width 832 \ + --num_frames 81 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-S2V-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-S2V-14B:wav2vec2-large-xlsr-53-english/model.safetensors,Wan-AI/Wan2.2-S2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-S2V-14B:Wan2.1_VAE.pth" \ + --audio_processor_path "Wan-AI/Wan2.2-S2V-14B:wav2vec2-large-xlsr-53-english/" \ + --learning_rate 1e-5 \ + --num_epochs 1 \ + --trainable_models "dit" \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-S2V-14B_full" \ + --extra_inputs "input_image,input_audio,s2v_pose_video" \ + --use_gradient_checkpointing_offload diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..c336cfee724b462d8e8a993622dd23e41d4e1f3d --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh @@ -0,0 +1,35 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-T2V-A14B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-T2V-A14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-T2V-A14B/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-T2V-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-T2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-T2V-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-T2V-A14B_high_noise_full" \ + --trainable_models "dit" \ + --max_timestep_boundary 0.417 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [875, 1000] + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-T2V-A14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-T2V-A14B/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-T2V-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-T2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-T2V-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-T2V-A14B_low_noise_full" \ + --trainable_models "dit" \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.417 +# boundary corresponds to timesteps [0, 875) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh new file mode 100644 index 0000000000000000000000000000000000000000..4cdcddf04943530400371cc4de098298fbb92363 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh @@ -0,0 +1,16 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-TI2V-5B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-TI2V-5B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-TI2V-5B/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-TI2V-5B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-TI2V-5B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-TI2V-5B:Wan2.2_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-TI2V-5B_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-VACE-Fun-A14B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-VACE-Fun-A14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..da834b150141ebd27f46e1820beca712df4d1d5d --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-VACE-Fun-A14B.sh @@ -0,0 +1,46 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-VACE-Fun-A14B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-VACE-Fun-A14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-VACE-Fun-A14B/metadata.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --num_frames 17 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-VACE-Fun-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-VACE-Fun-A14B:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-VACE-Fun-A14B:Wan2.1_VAE.pth" \ + --learning_rate 5e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.2-VACE-Fun-A14B_high_noise_full" \ + --trainable_models "vace" \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 \ + --initialize_model_on_cpu +# boundary corresponds to timesteps [900, 1000] +# The learning rate is kept consistent with the settings in the original paper + + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-VACE-Fun-A14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-VACE-Fun-A14B/metadata.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --num_frames 17 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-VACE-Fun-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-VACE-Fun-A14B:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-VACE-Fun-A14B:Wan2.1_VAE.pth" \ + --learning_rate 5e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.2-VACE-Fun-A14B_low_noise_full" \ + --trainable_models "vace" \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 \ + --initialize_model_on_cpu +# boundary corresponds to timesteps [0, 900] +# The learning rate is kept consistent with the settings in the original paper diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/accelerate_config_14B.yaml b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/accelerate_config_14B.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3875a9da2354631046baf19e61a9d5ab1d8d6aca --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/accelerate_config_14B.yaml @@ -0,0 +1,22 @@ +compute_environment: LOCAL_MACHINE +debug: false +deepspeed_config: + gradient_accumulation_steps: 1 + offload_optimizer_device: cpu + offload_param_device: cpu + zero3_init_flag: false + zero_stage: 2 +distributed_type: DEEPSPEED +downcast_bf16: 'no' +enable_cpu_affinity: false +machine_rank: 0 +main_training_function: main +mixed_precision: bf16 +num_machines: 1 +num_processes: 8 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/accelerate_config_zero3.yaml b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/accelerate_config_zero3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e6a8d273346c6e04e7ab97c8aa661914a0819a86 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/accelerate_config_zero3.yaml @@ -0,0 +1,23 @@ +compute_environment: LOCAL_MACHINE +debug: false +deepspeed_config: + gradient_accumulation_steps: 1 + offload_optimizer_device: none + offload_param_device: none + zero3_init_flag: true + zero3_save_16bit_model: true + zero_stage: 3 +distributed_type: DEEPSPEED +downcast_bf16: 'no' +enable_cpu_affinity: false +machine_rank: 0 +main_training_function: main +mixed_precision: bf16 +num_machines: 1 +num_processes: 8 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/krea-realtime-video.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/krea-realtime-video.sh new file mode 100644 index 0000000000000000000000000000000000000000..00a7027d3dcc14a9b7b2609c591d0e8882673079 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/full/krea-realtime-video.sh @@ -0,0 +1,14 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/krea-realtime-video/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/krea-realtime-video \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/krea-realtime-video/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "krea/krea-realtime-video:krea-realtime-video-14b.safetensors,Wan-AI/Wan2.1-T2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/krea-realtime-video_full" \ + --trainable_models "dit" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/LongCat-Video.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/LongCat-Video.sh new file mode 100644 index 0000000000000000000000000000000000000000..b50757003c068d0b75b65efb145d6a2c19f85445 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/LongCat-Video.sh @@ -0,0 +1,16 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/LongCat-Video/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/LongCat-Video \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/LongCat-Video/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "meituan-longcat/LongCat-Video:dit/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LongCat-Video_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "adaLN_modulation.1,attn.qkv,attn.proj,cross_attn.q_linear,cross_attn.kv_linear,cross_attn.proj,ffn.w1,ffn.w2,ffn.w3" \ + --lora_rank 32 diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Video-As-Prompt-Wan2.1-14B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Video-As-Prompt-Wan2.1-14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..4a8b4f4e46669a6ab771408a8a1a4a9e76076254 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Video-As-Prompt-Wan2.1-14B.sh @@ -0,0 +1,20 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Video-As-Prompt-Wan2.1-14B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Video-As-Prompt-Wan2.1-14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Video-As-Prompt-Wan2.1-14B/metadata.csv \ + --data_file_keys "video,vap_video" \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 10 \ + --model_id_with_origin_paths "ByteDance/Video-As-Prompt-Wan2.1-14B:transformer/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-720P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-720P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-720P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Video-As-Prompt-Wan2.1-14B_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "vap_video,input_image" \ + --use_gradient_checkpointing_offload diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan-Dancer-14B-global.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan-Dancer-14B-global.sh new file mode 100644 index 0000000000000000000000000000000000000000..b025de200a5c91e69b0a8c99d2ba4036726c0088 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan-Dancer-14B-global.sh @@ -0,0 +1,22 @@ +# 8*H200 required +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan-Dancer-14B-global/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-global \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-global/metadata.json \ + --data_file_keys "video,wantodance_reference_image,wantodance_keyframes,wantodance_music_path" \ + --height 1280 \ + --width 720 \ + --num_frames 149 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan-Dancer-14B:global_model.safetensors,Wan-AI/Wan-Dancer-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan-Dancer-14B:Wan2.1_VAE.pth,Wan-AI/Wan-Dancer-14B:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan-Dancer-14B-global_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "wantodance_music_path,wantodance_reference_image,wantodance_fps,wantodance_keyframes,wantodance_keyframes_mask,framewise_decoding" \ + --use_gradient_checkpointing_offload \ + --framewise_decoding diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan-Dancer-14B-local.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan-Dancer-14B-local.sh new file mode 100644 index 0000000000000000000000000000000000000000..36080da3b7f729cc722f412f924223a7acc6c013 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan-Dancer-14B-local.sh @@ -0,0 +1,21 @@ +# 8*H200 required +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan-Dancer-14B-local/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-local \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-local/metadata.json \ + --data_file_keys "video,wantodance_reference_image,wantodance_keyframes,wantodance_music_path" \ + --height 1280 \ + --width 720 \ + --num_frames 149 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan-Dancer-14B:local_model.safetensors,Wan-AI/Wan-Dancer-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan-Dancer-14B:Wan2.1_VAE.pth,Wan-AI/Wan-Dancer-14B:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan-Dancer-14B-local_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "wantodance_music_path,wantodance_reference_image,wantodance_fps,wantodance_keyframes,wantodance_keyframes_mask" \ + --use_gradient_checkpointing_offload diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh new file mode 100644 index 0000000000000000000000000000000000000000..eb0ffa7535930624641a7dd116fc24070e5d6b19 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh @@ -0,0 +1,17 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-1.3b-speedcontrol-v1/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-1.3b-speedcontrol-v1 \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-1.3b-speedcontrol-v1/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-1.3B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-1.3B:Wan2.1_VAE.pth,DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1:model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-1.3b-speedcontrol-v1_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "motion_bucket_id" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh new file mode 100644 index 0000000000000000000000000000000000000000..b6f9f28126f974e960c82b5ac460b83f89c553ab --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh @@ -0,0 +1,17 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-FLF2V-14B-720P/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-FLF2V-14B-720P \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-FLF2V-14B-720P/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-FLF2V-14B-720P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-FLF2V-14B-720P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-FLF2V-14B-720P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-FLF2V-14B-720P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-FLF2V-14B-720P_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,end_image" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..d6f38b7551ffb0b40b494d70f9a13a9de675ad9e --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh @@ -0,0 +1,18 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-Fun-1.3B-Control/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-1.3B-Control \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-1.3B-Control/metadata.csv \ + --data_file_keys "video,control_video" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-1.3B-Control:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-1.3B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-1.3B-Control:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-1.3B-Control:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-1.3B-Control_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "control_video" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh new file mode 100644 index 0000000000000000000000000000000000000000..067071aa572760b02d7913640227046c21eb3b39 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh @@ -0,0 +1,17 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-Fun-1.3B-InP/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-1.3B-InP \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-1.3B-InP/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-1.3B-InP:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-1.3B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-1.3B-InP:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-1.3B-InP:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-1.3B-InP_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,end_image" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..05cc1765f9aa5f32c12bb2a9fbc39b95587f9973 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh @@ -0,0 +1,18 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-Fun-14B-Control/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-14B-Control \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-14B-Control/metadata.csv \ + --data_file_keys "video,control_video" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-14B-Control:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-14B-Control:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-14B-Control:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-14B-Control_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "control_video" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh new file mode 100644 index 0000000000000000000000000000000000000000..c9a35a4859d25443123514c09ececb093ba79710 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh @@ -0,0 +1,17 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-Fun-14B-InP/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-14B-InP \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-14B-InP/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-14B-InP:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-14B-InP:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-14B-InP:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-14B-InP_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,end_image" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh new file mode 100644 index 0000000000000000000000000000000000000000..f6cfe4f856591659c51228d147d62286a02e5fec --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh @@ -0,0 +1,17 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-Fun-V1.1-1.3B-Control-Camera/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-1.3B-Control-Camera \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-1.3B-Control-Camera/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-1.3B-Control-Camera_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,camera_control_direction,camera_control_speed" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..f73e2bdf619f4e9780c841d864ecf6ed766b828c --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh @@ -0,0 +1,18 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-Fun-V1.1-1.3B-Control/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-1.3B-Control \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-1.3B-Control/metadata.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-1.3B-Control:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-1.3B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-1.3B-Control:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-1.3B-Control:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-1.3B-Control_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "control_video,reference_image" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh new file mode 100644 index 0000000000000000000000000000000000000000..42b2e5021ee011c4b9cb030e365b7de80b0dc472 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh @@ -0,0 +1,17 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-Fun-V1.1-1.3B-InP/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-1.3B-InP \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-1.3B-InP/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-1.3B-InP:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-1.3B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-1.3B-InP:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-1.3B-InP:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-1.3B-InP_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,end_image" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh new file mode 100644 index 0000000000000000000000000000000000000000..ae70d873597de460a865f564a18841f480b4c286 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh @@ -0,0 +1,17 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-Fun-V1.1-14B-Control-Camera/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-14B-Control-Camera \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-14B-Control-Camera/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-14B-Control-Camera:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-14B-Control-Camera:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-14B-Control-Camera:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-14B-Control-Camera:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-14B-Control-Camera_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,camera_control_direction,camera_control_speed" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..11a17b0471a5e0ff8e57ea3b1a310483705857d0 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh @@ -0,0 +1,18 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-Fun-V1.1-14B-Control/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-14B-Control \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-14B-Control/metadata.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-14B-Control:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-14B-Control:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-14B-Control:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-14B-Control_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "control_video,reference_image" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh new file mode 100644 index 0000000000000000000000000000000000000000..8767df918a033948738d6cf0accfc946f0adc9d4 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh @@ -0,0 +1,17 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-Fun-V1.1-14B-InP/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-14B-InP \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-Fun-V1.1-14B-InP/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-14B-InP:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-14B-InP:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-14B-InP:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-14B-InP_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,end_image" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh new file mode 100644 index 0000000000000000000000000000000000000000..eef3800c5639117fa1899f4fe56d98f507596b2c --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh @@ -0,0 +1,17 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-I2V-14B-480P/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-I2V-14B-480P \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-I2V-14B-480P/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-I2V-14B-480P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-480P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-480P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-480P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-I2V-14B-480P_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh new file mode 100644 index 0000000000000000000000000000000000000000..184108028a828f24e181663383c28a5edc7e8fdb --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh @@ -0,0 +1,21 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-I2V-14B-720P/*" --local_dir ./data/diffsynth_example_dataset + +# 1*80G GPU cannot train Wan2.2-Animate-14B LoRA +# We tested on 8*80G GPUs +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-I2V-14B-720P \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-I2V-14B-720P/metadata.csv \ + --height 720 \ + --width 1280 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-I2V-14B-720P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-720P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-720P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-720P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-I2V-14B-720P_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image" \ + --use_gradient_checkpointing_offload \ + --initialize_model_on_cpu diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh new file mode 100644 index 0000000000000000000000000000000000000000..8b1a2a6d45987f05c98730cdd0b772ba0f292ee2 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh @@ -0,0 +1,16 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-T2V-1.3B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-T2V-1.3B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-T2V-1.3B/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-1.3B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-1.3B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-T2V-1.3B_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..f8c5c52dfe4f0b4804589cf5b5fc6c8af3248bda --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh @@ -0,0 +1,16 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-T2V-14B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-T2V-14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-T2V-14B/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-T2V-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-T2V-14B_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh new file mode 100644 index 0000000000000000000000000000000000000000..1040a17097ebb16edb173d239f0382933ecc33b9 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh @@ -0,0 +1,19 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-VACE-1.3B-Preview/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-VACE-1.3B-Preview \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-VACE-1.3B-Preview/metadata.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "iic/VACE-Wan2.1-1.3B-Preview:diffusion_pytorch_model*.safetensors,iic/VACE-Wan2.1-1.3B-Preview:models_t5_umt5-xxl-enc-bf16.pth,iic/VACE-Wan2.1-1.3B-Preview:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.1-VACE-1.3B-Preview_lora" \ + --lora_base_model "vace" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh new file mode 100644 index 0000000000000000000000000000000000000000..f6e9891bcfc5fd130546c9ff6d17ead24ea16fa0 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh @@ -0,0 +1,19 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-VACE-1.3B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-VACE-1.3B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-VACE-1.3B/metadata.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-VACE-1.3B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-VACE-1.3B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-VACE-1.3B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.1-VACE-1.3B_lora" \ + --lora_base_model "vace" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..6c37a3b9756a00ff960340e17d6a3a0b184b2a4e --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh @@ -0,0 +1,20 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-VACE-14B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-VACE-14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-VACE-14B/metadata.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --num_frames 17 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-VACE-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-VACE-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-VACE-14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.1-VACE-14B_lora" \ + --lora_base_model "vace" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-Animate-14B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-Animate-14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..4e5d6ec6601267b7ce2fa4bf10db879b1451d4c2 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-Animate-14B.sh @@ -0,0 +1,22 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-Animate-14B/*" --local_dir ./data/diffsynth_example_dataset + +# 1*80G GPU cannot train Wan2.2-Animate-14B LoRA +# We tested on 8*80G GPUs +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Animate-14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Animate-14B/metadata.csv \ + --data_file_keys "video,animate_pose_video,animate_face_video" \ + --height 480 \ + --width 832 \ + --num_frames 81 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-Animate-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-Animate-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-Animate-14B:Wan2.1_VAE.pth,Wan-AI/Wan2.2-Animate-14B:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Animate-14B_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,animate_pose_video,animate_face_video" \ + --use_gradient_checkpointing_offload diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh new file mode 100644 index 0000000000000000000000000000000000000000..b982d392254e56c6931ffa09ed9c2efc53299614 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh @@ -0,0 +1,41 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-Fun-A14B-Control-Camera/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-Control-Camera \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-Control-Camera/metadata.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control-Camera:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control-Camera:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control-Camera:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-Control-Camera_high_noise_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,camera_control_direction,camera_control_speed" \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [900, 1000] + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-Control-Camera \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-Control-Camera/metadata.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control-Camera:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control-Camera:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control-Camera:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-Control-Camera_low_noise_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,camera_control_direction,camera_control_speed" \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 +# boundary corresponds to timesteps [0, 900] diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..1841aaaa8bcade408de5e22f86275903f7a3479a --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh @@ -0,0 +1,41 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-Fun-A14B-Control/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-Control \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-Control/metadata.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-Control_high_noise_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "control_video,reference_image" \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [900, 1000] + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-Control \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-Control/metadata.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-Control_low_noise_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "control_video,reference_image" \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 +# boundary corresponds to timesteps [0, 900] diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh new file mode 100644 index 0000000000000000000000000000000000000000..ccf5dbb377f398e0a99e903e48473a56efb6add1 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh @@ -0,0 +1,39 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-Fun-A14B-InP/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-InP \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-InP/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-InP:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-InP:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-InP_high_noise_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,end_image" \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [900, 1000] + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-InP \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-Fun-A14B-InP/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-InP:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-InP:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-InP_low_noise_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,end_image" \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 +# boundary corresponds to timesteps [0, 900] diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..d487d257c97f5ced8e18ef6b7fce107f5dc5fe8a --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh @@ -0,0 +1,41 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-I2V-A14B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-I2V-A14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-I2V-A14B/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-I2V-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-I2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-I2V-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-I2V-A14B_high_noise_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image" \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [900, 1000] + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-I2V-A14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-I2V-A14B/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-I2V-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-I2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-I2V-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-I2V-A14B_low_noise_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image" \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 +# boundary corresponds to timesteps [0, 900) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-S2V-14B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-S2V-14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..1f819f54e94831b6738091e6d816d7d565791cc7 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-S2V-14B.sh @@ -0,0 +1,21 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-S2V-14B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-S2V-14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-S2V-14B/metadata.csv \ + --data_file_keys "video,input_audio,s2v_pose_video" \ + --height 448 \ + --width 832 \ + --num_frames 81 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-S2V-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-S2V-14B:wav2vec2-large-xlsr-53-english/model.safetensors,Wan-AI/Wan2.2-S2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-S2V-14B:Wan2.1_VAE.pth" \ + --audio_processor_path "Wan-AI/Wan2.2-S2V-14B:wav2vec2-large-xlsr-53-english/" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-S2V-14B_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,input_audio,s2v_pose_video" \ + --use_gradient_checkpointing_offload diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..ed1b4a36763d4f121e33c57eab7d13416cbbe20b --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh @@ -0,0 +1,40 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-T2V-A14B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-T2V-A14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-T2V-A14B/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-T2V-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-T2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-T2V-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-T2V-A14B_high_noise_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --max_timestep_boundary 0.417 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [875, 1000] + + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-T2V-A14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-T2V-A14B/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-T2V-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-T2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-T2V-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-T2V-A14B_low_noise_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.417 +# boundary corresponds to timesteps [0, 875) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh new file mode 100644 index 0000000000000000000000000000000000000000..fe733a25b1b0c32049efd0e9145dcdab1ade150b --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh @@ -0,0 +1,18 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-TI2V-5B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-TI2V-5B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-TI2V-5B/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-TI2V-5B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-TI2V-5B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-TI2V-5B:Wan2.2_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-TI2V-5B_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-VACE-Fun-A14B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-VACE-Fun-A14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..dba43e5c207d69712fdb4030c3e8386f3c6d0f63 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/Wan2.2-VACE-Fun-A14B.sh @@ -0,0 +1,45 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-VACE-Fun-A14B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-VACE-Fun-A14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-VACE-Fun-A14B/metadata.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --num_frames 17 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-VACE-Fun-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-VACE-Fun-A14B:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-VACE-Fun-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.2-VACE-Fun-A14B_high_noise_lora" \ + --lora_base_model "vace" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [900, 1000] + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-VACE-Fun-A14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-VACE-Fun-A14B/metadata.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --num_frames 17 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-VACE-Fun-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-VACE-Fun-A14B:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-VACE-Fun-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.2-VACE-Fun-A14B_low_noise_lora" \ + --lora_base_model "vace" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 +# boundary corresponds to timesteps [0, 900] diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/krea-realtime-video.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/krea-realtime-video.sh new file mode 100644 index 0000000000000000000000000000000000000000..b4526126b07dec2e030043a91c3d511681301e49 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/lora/krea-realtime-video.sh @@ -0,0 +1,16 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/krea-realtime-video/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/krea-realtime-video \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/krea-realtime-video/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "krea/krea-realtime-video:krea-realtime-video-14b.safetensors,Wan-AI/Wan2.1-T2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/krea-realtime-video_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/direct_distill/Wan2.1-T2V-1.3B.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/direct_distill/Wan2.1-T2V-1.3B.sh new file mode 100644 index 0000000000000000000000000000000000000000..e01b85a3882eb1a4eb236f109fc4e05eb1bcea2c --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/direct_distill/Wan2.1-T2V-1.3B.sh @@ -0,0 +1,16 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-T2V-1.3B_direct_distill/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-T2V-1.3B_direct_distill \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-T2V-1.3B_direct_distill/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 160 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-1.3B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-1.3B:Wan2.1_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-T2V-1.3B_full_distill" \ + --trainable_models "dit" \ + --task "direct_distill" \ + --extra_inputs "seed,rand_device,num_inference_steps,cfg_scale" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/direct_distill/validate.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/direct_distill/validate.py new file mode 100644 index 0000000000000000000000000000000000000000..6da0e1b1722564fe5e7751f31dd7bd0272f41ada --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/direct_distill/validate.py @@ -0,0 +1,23 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig("models/train/Wan2.1-T2V-1.3B_full_distill/epoch-1.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) + +video = pipe( + prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。", + cfg_scale=1, num_inference_steps=4, + seed=0, tiled=True, +) +save_video(video, "video_distill_Wan2.1-T2V-1.3B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/fp8_training/Wan2.1-I2V-14B-480P.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/fp8_training/Wan2.1-I2V-14B-480P.sh new file mode 100644 index 0000000000000000000000000000000000000000..44328755509d6e28c230daae73188c0db8cd5542 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/fp8_training/Wan2.1-I2V-14B-480P.sh @@ -0,0 +1,18 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-I2V-14B-480P/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-I2V-14B-480P \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-I2V-14B-480P/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-I2V-14B-480P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-480P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-480P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-480P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-I2V-14B-480P_lora_fp8" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image" \ + --fp8_models "Wan-AI/Wan2.1-I2V-14B-480P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-480P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-480P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-480P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ No newline at end of file diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/fp8_training/validate.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/fp8_training/validate.py new file mode 100644 index 0000000000000000000000000000000000000000..0fa4b0f2800cdf0eabb29512de91d8107712bdda --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/fp8_training/validate.py @@ -0,0 +1,28 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-I2V-14B-480P_lora_fp8/epoch-4.safetensors", alpha=1) + +input_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=input_image, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-I2V-14B-480P.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/low_vram_training/Wan2.1-I2V-14B-480P.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/low_vram_training/Wan2.1-I2V-14B-480P.sh new file mode 100644 index 0000000000000000000000000000000000000000..bdfa8027adf5570c71a00640b5efd3ce95f42b02 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/low_vram_training/Wan2.1-I2V-14B-480P.sh @@ -0,0 +1,40 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-I2V-14B-480P/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-I2V-14B-480P \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-I2V-14B-480P/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 1 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-I2V-14B-480P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-480P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-480P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-480P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-I2V-14B-480P_lora_lowvram_cache" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image" \ + --task "sft:data_process" \ + --offload_models "Wan-AI/Wan2.1-I2V-14B-480P:diffusion_pytorch_model*.safetensors" \ + --fp8_models "Wan-AI/Wan2.1-I2V-14B-480P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-480P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-480P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --use_gradient_checkpointing_offload + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path "./models/train/Wan2.1-I2V-14B-480P_lora_split_cache" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-I2V-14B-480P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-480P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-480P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-480P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-I2V-14B-480P_lora_lowvram" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image" \ + --task "sft:train" \ + --offload_models "Wan-AI/Wan2.1-I2V-14B-480P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-480P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-480P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --fp8_models "Wan-AI/Wan2.1-I2V-14B-480P:diffusion_pytorch_model*.safetensors" \ + --use_gradient_checkpointing_offload diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/low_vram_training/validate.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/low_vram_training/validate.py new file mode 100644 index 0000000000000000000000000000000000000000..e5d59c40f8ae734ac56e91458a5b0c6f5247f916 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/low_vram_training/validate.py @@ -0,0 +1,28 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-I2V-14B-480P_lora_lowvram/epoch-4.safetensors", alpha=1) + +input_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=input_image, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-I2V-14B-480P.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/npu_training/Wan2.1-T2V-14B-NPU.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/npu_training/Wan2.1-T2V-14B-NPU.sh new file mode 100644 index 0000000000000000000000000000000000000000..8be7469e048ec999dfe84431788e4a8f6403f066 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/npu_training/Wan2.1-T2V-14B-NPU.sh @@ -0,0 +1,18 @@ +export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True +export CPU_AFFINITY_CONF=1 + +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-T2V-14B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-T2V-14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-T2V-14B/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-T2V-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-T2V-14B_full" \ + --trainable_models "dit" \ + --initialize_model_on_cpu \ No newline at end of file diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/npu_training/Wan2.2-T2V-A14B-NPU.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/npu_training/Wan2.2-T2V-A14B-NPU.sh new file mode 100644 index 0000000000000000000000000000000000000000..989bd4e8798b9e44e2d0d5911763b5f3219892c3 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/npu_training/Wan2.2-T2V-A14B-NPU.sh @@ -0,0 +1,40 @@ +export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True +export CPU_AFFINITY_CONF=1 + +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-T2V-A14B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-T2V-A14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-T2V-A14B/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-T2V-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-T2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-T2V-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-T2V-A14B_high_noise_full" \ + --trainable_models "dit" \ + --max_timestep_boundary 0.417 \ + --min_timestep_boundary 0 \ + --initialize_model_on_cpu +# boundary corresponds to timesteps [875, 1000] + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-T2V-A14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-T2V-A14B/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-T2V-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-T2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-T2V-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-T2V-A14B_low_noise_full" \ + --trainable_models "dit" \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.417 \ + --initialize_model_on_cpu +# boundary corresponds to timesteps [0, 875) \ No newline at end of file diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/npu_training/Wan2.2-VACE-Fun-A14B-NPU.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/npu_training/Wan2.2-VACE-Fun-A14B-NPU.sh new file mode 100644 index 0000000000000000000000000000000000000000..b72fdfc0fa0d4b14bc77055886f569234062b591 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/npu_training/Wan2.2-VACE-Fun-A14B-NPU.sh @@ -0,0 +1,47 @@ +export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True +export CPU_AFFINITY_CONF=1 + +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-VACE-Fun-A14B/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-VACE-Fun-A14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-VACE-Fun-A14B/metadata.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --num_frames 17 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-VACE-Fun-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-VACE-Fun-A14B:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-VACE-Fun-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.2-VACE-Fun-A14B_high_noise_full" \ + --trainable_models "vace" \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 \ + --initialize_model_on_cpu +# boundary corresponds to timesteps [900, 1000] + + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-VACE-Fun-A14B \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-VACE-Fun-A14B/metadata.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --num_frames 17 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-VACE-Fun-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-VACE-Fun-A14B:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-VACE-Fun-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.2-VACE-Fun-A14B_low_noise_full" \ + --trainable_models "vace" \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 \ + --initialize_model_on_cpu +# boundary corresponds to timesteps [0, 900] \ No newline at end of file diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/split_training/Wan2.1-I2V-14B-480P.sh b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/split_training/Wan2.1-I2V-14B-480P.sh new file mode 100644 index 0000000000000000000000000000000000000000..36ff935c4b2a0df8f594d3260c43c8ccb576f3ce --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/split_training/Wan2.1-I2V-14B-480P.sh @@ -0,0 +1,36 @@ +modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-I2V-14B-480P/*" --local_dir ./data/diffsynth_example_dataset + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-I2V-14B-480P \ + --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-I2V-14B-480P/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 1 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-I2V-14B-480P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-480P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-480P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-480P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-I2V-14B-480P_lora_split_cache" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image" \ + --task "sft:data_process" \ + --offload_models "Wan-AI/Wan2.1-I2V-14B-480P:diffusion_pytorch_model*.safetensors" + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path "./models/train/Wan2.1-I2V-14B-480P_lora_split_cache" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-I2V-14B-480P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-480P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-480P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-480P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-I2V-14B-480P_lora_split" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image" \ + --task "sft:train" \ + --offload_models "Wan-AI/Wan2.1-I2V-14B-480P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-480P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-480P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/split_training/validate.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/split_training/validate.py new file mode 100644 index 0000000000000000000000000000000000000000..7377277517feb81ab7abbc942085f14107b96119 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/special/split_training/validate.py @@ -0,0 +1,28 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-I2V-14B-480P_lora_split/epoch-4.safetensors", alpha=1) + +input_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=input_image, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-I2V-14B-480P.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/train.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/train.py new file mode 100644 index 0000000000000000000000000000000000000000..ce8fedcd59bc166e9ff14a05cd4ccfa29938f8e1 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/train.py @@ -0,0 +1,199 @@ +import torch, os, argparse, accelerate, warnings +from diffsynth.core import UnifiedDataset +from diffsynth.core.data.operators import LoadVideo, LoadAudio, ImageCropAndResize, ToAbsolutePath +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from diffsynth.diffusion import * +os.environ["TOKENIZERS_PARALLELISM"] = "false" + + +class WanTrainingModule(DiffusionTrainingModule): + def __init__( + self, + model_paths=None, model_id_with_origin_paths=None, + tokenizer_path=None, audio_processor_path=None, + trainable_models=None, + lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None, + preset_lora_path=None, preset_lora_model=None, + use_gradient_checkpointing=True, + use_gradient_checkpointing_offload=False, + extra_inputs=None, + fp8_models=None, + offload_models=None, + resume_from_checkpoint=None, remove_prefix_in_ckpt=None, + device="cpu", + task="sft", + max_timestep_boundary=1.0, + min_timestep_boundary=0.0, + ): + super().__init__() + # Warning + if not use_gradient_checkpointing: + warnings.warn("Gradient checkpointing is detected as disabled. To prevent out-of-memory errors, the training framework will forcibly enable gradient checkpointing.") + use_gradient_checkpointing = True + + # Load models + model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, fp8_models=fp8_models, offload_models=offload_models, device=device) + tokenizer_config = ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/") if tokenizer_path is None else ModelConfig(tokenizer_path) + audio_processor_config = self.parse_path_or_model_id(audio_processor_path) + self.pipe = WanVideoPipeline.from_pretrained(torch_dtype=torch.bfloat16, device=device, model_configs=model_configs, tokenizer_config=tokenizer_config, audio_processor_config=audio_processor_config) + self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model) + self.resume_from_checkpoint(resume_from_checkpoint, remove_prefix_in_ckpt) + + # Training mode + self.switch_pipe_to_training_mode( + self.pipe, trainable_models, + lora_base_model, lora_target_modules, lora_rank, lora_checkpoint, + preset_lora_path, preset_lora_model, + task=task, + ) + + # Store other configs + self.use_gradient_checkpointing = use_gradient_checkpointing + self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload + self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else [] + self.fp8_models = fp8_models + self.task = task + self.task_to_loss = { + "sft:data_process": lambda pipe, *args: args, + "direct_distill:data_process": lambda pipe, *args: args, + "sft": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi), + "sft:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi), + "direct_distill": lambda pipe, inputs_shared, inputs_posi, inputs_nega: DirectDistillLoss(pipe, **inputs_shared, **inputs_posi), + "direct_distill:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: DirectDistillLoss(pipe, **inputs_shared, **inputs_posi), + } + self.max_timestep_boundary = max_timestep_boundary + self.min_timestep_boundary = min_timestep_boundary + + def parse_extra_inputs(self, data, extra_inputs, inputs_shared): + for extra_input in extra_inputs: + if extra_input == "input_image": + inputs_shared["input_image"] = data["video"][0] + elif extra_input == "end_image": + inputs_shared["end_image"] = data["video"][-1] + elif extra_input == "reference_image" or extra_input == "vace_reference_image": + inputs_shared[extra_input] = data[extra_input][0] + else: + inputs_shared[extra_input] = data[extra_input] + if inputs_shared.get("framewise_decoding", False): + # WanToDance global model + inputs_shared["num_frames"] = 4 * (len(data["video"]) - 1) + 1 + return inputs_shared + + def get_pipeline_inputs(self, data): + inputs_posi = {"prompt": data["prompt"]} + inputs_nega = {} + inputs_shared = { + # Assume you are using this pipeline for inference, + # please fill in the input parameters. + "input_video": data["video"], + "height": data["video"][0].size[1], + "width": data["video"][0].size[0], + "num_frames": len(data["video"]), + # Please do not modify the following parameters + # unless you clearly know what this will cause. + "cfg_scale": 1, + "tiled": False, + "rand_device": self.pipe.device, + "use_gradient_checkpointing": self.use_gradient_checkpointing, + "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload, + "cfg_merge": False, + "vace_scale": 1, + "max_timestep_boundary": self.max_timestep_boundary, + "min_timestep_boundary": self.min_timestep_boundary, + } + inputs_shared = self.parse_extra_inputs(data, self.extra_inputs, inputs_shared) + return inputs_shared, inputs_posi, inputs_nega + + def forward(self, data, inputs=None): + if inputs is None: inputs = self.get_pipeline_inputs(data) + inputs = self.transfer_data_to_device(inputs, self.pipe.device, self.pipe.torch_dtype) + for unit in self.pipe.units: + inputs = self.pipe.unit_runner(unit, self.pipe, *inputs) + loss = self.task_to_loss[self.task](self.pipe, *inputs) + return loss + + +def wan_parser(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser = add_general_config(parser) + parser = add_video_size_config(parser) + parser.add_argument("--tokenizer_path", type=str, default=None, help="Path to tokenizer.") + parser.add_argument("--audio_processor_path", type=str, default=None, help="Path to the audio processor. If provided, the processor will be used for Wan2.2-S2V model.") + parser.add_argument("--max_timestep_boundary", type=float, default=1.0, help="Max timestep boundary (for mixed models, e.g., Wan-AI/Wan2.2-I2V-A14B).") + parser.add_argument("--min_timestep_boundary", type=float, default=0.0, help="Min timestep boundary (for mixed models, e.g., Wan-AI/Wan2.2-I2V-A14B).") + parser.add_argument("--initialize_model_on_cpu", default=False, action="store_true", help="Whether to initialize models on CPU.") + parser.add_argument("--framewise_decoding", default=False, action="store_true", help="Enable it if this model is a WanToDance global model.") + return parser + + +if __name__ == "__main__": + parser = wan_parser() + args = parser.parse_args() + accelerator = accelerate.Accelerator( + gradient_accumulation_steps=args.gradient_accumulation_steps, + kwargs_handlers=[accelerate.DistributedDataParallelKwargs(find_unused_parameters=args.find_unused_parameters)], + ) + dataset = UnifiedDataset( + base_path=args.dataset_base_path, + metadata_path=args.dataset_metadata_path, + repeat=args.dataset_repeat, + data_file_keys=args.data_file_keys.split(","), + main_data_operator=UnifiedDataset.default_video_operator( + base_path=args.dataset_base_path, + max_pixels=args.max_pixels, + height=args.height, + width=args.width, + height_division_factor=16, + width_division_factor=16, + num_frames=args.num_frames, + time_division_factor=4 if not args.framewise_decoding else 1, + time_division_remainder=1 if not args.framewise_decoding else 0, + ), + special_operator_map={ + "animate_face_video": ToAbsolutePath(args.dataset_base_path) >> LoadVideo(args.num_frames, 4, 1, frame_processor=ImageCropAndResize(512, 512, None, 16, 16)), + "input_audio": ToAbsolutePath(args.dataset_base_path) >> LoadAudio(sr=16000), + "wantodance_music_path": ToAbsolutePath(args.dataset_base_path), + } + ) + model = WanTrainingModule( + model_paths=args.model_paths, + model_id_with_origin_paths=args.model_id_with_origin_paths, + tokenizer_path=args.tokenizer_path, + audio_processor_path=args.audio_processor_path, + trainable_models=args.trainable_models, + lora_base_model=args.lora_base_model, + lora_target_modules=args.lora_target_modules, + lora_rank=args.lora_rank, + lora_checkpoint=args.lora_checkpoint, + preset_lora_path=args.preset_lora_path, + preset_lora_model=args.preset_lora_model, + use_gradient_checkpointing=args.use_gradient_checkpointing, + use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload, + extra_inputs=args.extra_inputs, + fp8_models=args.fp8_models, + offload_models=args.offload_models, + resume_from_checkpoint=args.resume_from_checkpoint, + remove_prefix_in_ckpt=args.remove_prefix_in_ckpt, + task=args.task, + device="cpu" if (args.initialize_model_on_cpu or args.enable_model_cpu_offload) else accelerator.device, + max_timestep_boundary=args.max_timestep_boundary, + min_timestep_boundary=args.min_timestep_boundary, + ) + model_logger = ModelLogger( + args.output_path, + remove_prefix_in_ckpt=args.remove_prefix_in_ckpt, + enable_tensorboard_log=args.enable_tensorboard_log, + enable_swanlab_log=args.enable_swanlab_log, + swanlab_project=args.swanlab_project, + enable_wandb_log=args.enable_wandb_log, + wandb_project=args.wandb_project, + ) + launcher_map = { + "sft:data_process": launch_data_process_task, + "direct_distill:data_process": launch_data_process_task, + "sft": launch_training_task, + "sft:train": launch_training_task, + "direct_distill": launch_training_task, + "direct_distill:train": launch_training_task, + } + launcher_map[args.task](accelerator, dataset, model, model_logger, args=args) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/LongCat-Video.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/LongCat-Video.py new file mode 100644 index 0000000000000000000000000000000000000000..62e62e2c28f8c2e930d07686988f45bed65d8bc5 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/LongCat-Video.py @@ -0,0 +1,25 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="meituan-longcat/LongCat-Video", origin_file_pattern="dit/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +state_dict = load_state_dict("models/train/LongCat-Video_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + seed=1, tiled=True +) +save_video(video, "video_LongCat-Video.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Video-As-Prompt-Wan2.1-14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Video-As-Prompt-Wan2.1-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..757b2d9ccf4f741f2051af8399b15efb4762f8a4 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Video-As-Prompt-Wan2.1-14B.py @@ -0,0 +1,43 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="ByteDance/Video-As-Prompt-Wan2.1-14B", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +state_dict = load_state_dict("models/train/Video-As-Prompt-Wan2.1-14B_full/epoch-1.safetensors") +pipe.vap.load_state_dict(state_dict) + +ref_video_path = 'data/example_video_dataset/wanvap/vap_ref.mp4' +target_image_path = 'data/example_video_dataset/wanvap/input_image.jpg' + +image = Image.open(target_image_path).convert("RGB") +ref_video = VideoData(ref_video_path, height=480, width=832) +ref_frames = [ref_video[i] for i in range(49)] + +vap_prompt = "A man stands with his back to the camera on a dirt path overlooking sun-drenched, rolling green tea plantations. He wears a blue and green plaid shirt, dark pants, and white shoes. As he turns to face the camera and spreads his arms, a brief, magical burst of sparkling golden light particles envelops him. Through this shimmer, he seamlessly transforms into a Labubu toy character. His head morphs into the iconic large, furry-eared head of the toy, featuring a wide grin with pointed teeth and red cheek markings. The character retains the man's original plaid shirt and clothing, which now fit its stylized, cartoonish body. The camera remains static throughout the transformation, positioned low among the tea bushes, maintaining a consistent view of the subject and the expansive scenery." +prompt = "A young woman with curly hair, wearing a green hijab and a floral dress, plays a violin in front of a vintage green car on a tree-lined street. She executes a swift counter-clockwise turn to face the camera. During the turn, a brilliant shower of golden, sparkling particles erupts and momentarily obscures her figure. As the particles fade, she is revealed to have seamlessly transformed into a Labubu toy character. This new figure, now with the toy's signature large ears, big eyes, and toothy grin, maintains the original pose and continues playing the violin. The character's clothing—the green hijab, floral dress, and black overcoat—remains identical to the woman's. Throughout this transition, the camera stays static, and the street-side environment remains completely consistent." +negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards" + +video = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + input_image=image, + seed=42, tiled=True, + height=480, width=832, + num_frames=49, + vap_video=ref_frames, + vap_prompt=vap_prompt, + negative_vap_prompt=negative_prompt, +) +save_video(video, "video_Video-As-Prompt-Wan2.1-14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan-Dancer-14B-global.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan-Dancer-14B-global.py new file mode 100644 index 0000000000000000000000000000000000000000..339324fd93fe52cbc7f78719ccf9c2e62998a10c --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan-Dancer-14B-global.py @@ -0,0 +1,51 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download +from diffsynth.core import load_state_dict + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="global_model.safetensors"), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) +state_dict = load_state_dict("models/train/Wan-Dancer-14B-global_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +dataset_snapshot_download( + "DiffSynth-Studio/diffsynth_example_dataset", + local_dir="data/diffsynth_example_dataset", + allow_file_pattern="wanvideo/Wan-Dancer-14B-global/*" +) +# This is a specialized model with the following constraints on its input parameters: +# * The model outputs a sequence of keyframes rather than a video; therefore, `framewise_decoding=True` must be set. +# * When the number of keyframes is $n$, `num_frames` = 4 * (n - 1) + 1. +# * Reducing `height`, `width`, `num_frames`, or `num_inference_steps` may lead to severe artifacts or generation failure. +# * The audio file specified by `wantodance_music_path` must match the video duration, calculated as (`num_frames` / 7.5) seconds. +# * The width and height of `wantodance_reference_image` must be multiples of 16. +# * `wantodance_fps` is configurable, but since the model appears to have been trained exclusively at 7.5 FPS, setting it to other values is not recommended. +# * The first frame of `wantodance_keyframes` is the `wantodance_reference_image`, while all subsequent frames are solid black. +# * `wantodance_keyframes_mask` indicates the positions of valid frames within `wantodance_keyframes`. +wantodance_keyframes = VideoData("data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-global/keyframes.mp4") +wantodance_keyframes = [wantodance_keyframes[i] for i in range(149)] +video = pipe( + prompt="一个人正在跳舞，舞蹈种类是韩舞。帧率是7.5000", + 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", + seed=0, tiled=False, + height=1280, width=720, num_frames=149, + num_inference_steps=48, + wantodance_music_path="data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-global/music.WAV", + wantodance_reference_image=Image.open("data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-global/refimage.jpg"), + wantodance_fps=7.5, + wantodance_keyframes=wantodance_keyframes, + wantodance_keyframes_mask=[1] + [0] * 148, + framewise_decoding=True, +) +save_video(video, "video_Wan-Dancer-14B-global.mp4", fps=7.5, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan-Dancer-14B-local.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan-Dancer-14B-local.py new file mode 100644 index 0000000000000000000000000000000000000000..3c736070ee8a1fe147423bfd18dc0ec453d7ca87 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan-Dancer-14B-local.py @@ -0,0 +1,55 @@ +import torch, os +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download +from diffsynth.core import load_state_dict + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="local_model.safetensors"), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) +state_dict = load_state_dict("models/train/Wan-Dancer-14B-local_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +dataset_snapshot_download( + "DiffSynth-Studio/diffsynth_example_dataset", + local_dir="data/diffsynth_example_dataset", + allow_file_pattern="wanvideo/Wan-Dancer-14B-local/*" +) +# This is a specialized model with the following constraints on its input parameters: +# * The model renders and outputs video based on a sequence of keyframes; therefore, `wantodance_keyframes` must be provided correctly. +# * If you need to generate a long video, please generate it in segments, and ensure that `wantodance_music_path`, `wantodance_keyframes`, and `wantodance_keyframes_mask` are properly split accordingly. +# * The audio file specified by `wantodance_music_path` must match the video duration, calculated as (`num_frames` / 30) seconds. +# * The width and height of `wantodance_reference_image` must be multiples of 16. +# * `wantodance_fps` is configurable, but since the model appears to have been trained exclusively at 30 FPS, setting it to other values is not recommended. +# * In `wantodance_keyframes`, frames that are not keyframes should be solid black. +# * `wantodance_keyframes_mask` indicates the positions of valid frames within `wantodance_keyframes`. +wantodance_keyframes = VideoData("data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-local/keyframes.mp4") +wantodance_keyframes = [wantodance_keyframes[i] for i in range(149)] +video = pipe( + prompt="一个人正在跳舞，舞蹈种类是古典舞,图像清晰程度高,人物动作平均幅度中等,人物动作最大幅度中等。, 帧率是30fps。", + 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", + seed=0, tiled=True, + height=1280, width=720, num_frames=149, + num_inference_steps=24, + wantodance_music_path="data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-local/music.wav", + wantodance_reference_image=Image.open("data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-local/refimage.jpg"), + wantodance_fps=30, + wantodance_keyframes=wantodance_keyframes, + wantodance_keyframes_mask=[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1], +) +save_video(video, "video_Wan-Dancer-14B-local.mp4", fps=30, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py new file mode 100644 index 0000000000000000000000000000000000000000..8aef365f3f1e22d6fc1be0db8b3799e28019f49a --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py @@ -0,0 +1,28 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1", origin_file_pattern="model.safetensors"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-1.3b-speedcontrol-v1_full/epoch-1.safetensors") +pipe.motion_controller.load_state_dict(state_dict) + +# Text-to-video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + seed=1, tiled=True, + motion_bucket_id=50 +) +save_video(video, "video_Wan2.1-1.3b-speedcontrol-v1.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py new file mode 100644 index 0000000000000000000000000000000000000000..12ac16234e120d4817883059ed1ee2d13b0b9d59 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py @@ -0,0 +1,33 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-FLF2V-14B-720P_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=video[0], + end_image=video[80], + seed=0, tiled=True, + sigma_shift=16, +) +save_video(video, "video_Wan2.1-FLF2V-14B-720P.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..c6de6037df9b2eb2d65cda3cd3f10bf776bd33a5 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py @@ -0,0 +1,32 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-Fun-1.3B-Control_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(81)] + +# Control video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + control_video=video, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-1.3B-Control.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..766d436d209170bd45ab969e1d53dd52b1faf67d --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-Fun-1.3B-InP_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=video[0], end_image=video[80], + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-1.3B-InP.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..d05da17e962da4a0fbd756818635d90196f41794 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py @@ -0,0 +1,32 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-Fun-14B-Control_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(81)] + +# Control video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + control_video=video, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-14B-Control.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..f1953ae796fab947526a7779ff034cdd75e880c7 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-Fun-14B-InP_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=video[0], end_image=video[80], + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-14B-InP.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..ee547dcf4e0a21a58b4aef30e78d3c2ef43ce926 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py @@ -0,0 +1,32 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-Fun-V1.1-1.3B-Control-Camera_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=video[0], + camera_control_direction="Left", camera_control_speed=0.0, + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-1.3B-Control-Camera.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..34a25918c7e3732068f4c222951190d0227b5b02 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py @@ -0,0 +1,33 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-Fun-V1.1-1.3B-Control_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(81)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +# Control video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + control_video=video, reference_image=reference_image, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-1.3B-Control.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..106d403c65c5af42a0250dbbbb73d57fdfb1333e --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-Fun-V1.1-1.3B-InP_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=video[0], end_image=video[80], + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-1.3B-InP.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..4a14a5d3c7935220cea100497c6727da62745d9c --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py @@ -0,0 +1,32 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-Fun-V1.1-14B-Control-Camera_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=video[0], + camera_control_direction="Left", camera_control_speed=0.0, + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-14B-Control-Camera.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..dc916970d6b33ddbfe0643e5b920bf88f797b796 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py @@ -0,0 +1,33 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-Fun-V1.1-14B-Control_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(81)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +# Control video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + control_video=video, reference_image=reference_image, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-14B-Control.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..5571b5dbd812e6ea82b5b07cc68ce7334835541a --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-Fun-V1.1-14B-InP_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=video[0], end_image=video[80], + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-14B-InP.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py new file mode 100644 index 0000000000000000000000000000000000000000..7562b4c23ff1e9d3842038d0461d8786bafe06b8 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py @@ -0,0 +1,30 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-I2V-14B-480P_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +input_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=input_image, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-I2V-14B-480P.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py new file mode 100644 index 0000000000000000000000000000000000000000..3ca5a3886ffd1bdd47c4f46b930d1b281304b6cb --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-I2V-14B-720P_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +input_image = VideoData("data/example_video_dataset/video1.mp4", height=720, width=1280)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=input_image, + height=720, width=1280, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-I2V-14B-720P.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..17f87fa1272d41220d81ae5bd6acaf11cc425c16 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py @@ -0,0 +1,25 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-T2V-1.3B_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-T2V-1.3B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..8b9032d4540fb76556eaa7b2eb0a3d50872a4231 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py @@ -0,0 +1,25 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-T2V-14B_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-T2V-14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py new file mode 100644 index 0000000000000000000000000000000000000000..8ba0067a91e19e3c313f9aef1382de34b54e815c --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py @@ -0,0 +1,30 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-VACE-1.3B-Preview_full/epoch-1.safetensors") +pipe.vace.load_state_dict(state_dict) + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(49)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + vace_video=video, vace_reference_image=reference_image, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-VACE-1.3B-Preview.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..add5ff9ad6ff2cdd55933c9cbf33e256c551c2ff --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py @@ -0,0 +1,30 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-VACE-1.3B_full/epoch-1.safetensors") +pipe.vace.load_state_dict(state_dict) + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(49)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + vace_video=video, vace_reference_image=reference_image, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-VACE-1.3B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..a861fa3d2a642bcf6dd2040be4bd40c80c0fcd50 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py @@ -0,0 +1,30 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-VACE-14B_full/epoch-1.safetensors") +pipe.vace.load_state_dict(state_dict) + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(17)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + vace_video=video, vace_reference_image=reference_image, num_frames=17, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-VACE-14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-Animate-14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-Animate-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..0cdce065692c8f2983b05a1e302cf4391a4a9645 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-Animate-14B.py @@ -0,0 +1,33 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.2-Animate-14B_full/epoch-1.safetensors") +pipe.animate_adapter.load_state_dict(state_dict, strict=False) + +input_image = VideoData("data/example_video_dataset/animate/animate_output.mp4", height=480, width=832)[0] +animate_pose_video = VideoData("data/examples/wan/animate/animate_pose_video.mp4", height=480, width=832).raw_data()[:81-4] +animate_face_video = VideoData("data/examples/wan/animate/animate_face_video.mp4", height=512, width=512).raw_data()[:81-4] +video = pipe( + prompt="视频中的人在做动作", + seed=0, tiled=True, + input_image=input_image, + animate_pose_video=animate_pose_video, + animate_face_video=animate_face_video, + num_frames=81, height=480, width=832, + num_inference_steps=20, cfg_scale=1, +) +save_video(video, "video_Wan2.2-Animate-14B.mp4", fps=15, quality=5) \ No newline at end of file diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..ce52086c5f38a401820517eb88f8c2500f9bc23c --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py @@ -0,0 +1,34 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.2-Fun-A14B-Control-Camera_high_noise_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +state_dict = load_state_dict("models/train/Wan2.2-Fun-A14B-Control-Camera_low_noise_full/epoch-1.safetensors") +pipe.dit2.load_state_dict(state_dict) + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=video[0], + camera_control_direction="Left", camera_control_speed=0.0, + seed=0, tiled=True +) +save_video(video, "video_Wan2.2-Fun-A14B-Control-Camera.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..ee32d9c6129687a4c18fb40fe5f5ebe06e78cdf1 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py @@ -0,0 +1,35 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.2-Fun-A14B-Control_high_noise_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +state_dict = load_state_dict("models/train/Wan2.2-Fun-A14B-Control_low_noise_full/epoch-1.safetensors") +pipe.dit2.load_state_dict(state_dict) + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(81)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +# Control video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + control_video=video, reference_image=reference_image, + seed=1, tiled=True +) +save_video(video, "video_Wan2.2-Fun-A14B-Control.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..678fb5576415cc7e9ab6ae94ff01366cf957d1c1 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py @@ -0,0 +1,32 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.2-Fun-A14B-InP_high_noise_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +state_dict = load_state_dict("models/train/Wan2.2-Fun-A14B-InP_low_noise_full/epoch-1.safetensors") +pipe.dit2.load_state_dict(state_dict) +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=video[0], end_image=video[80], + seed=0, tiled=True +) +save_video(video, "video_Wan2.2-Fun-A14B-InP.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py new file mode 100644 index 0000000000000000000000000000000000000000..26243415dcce900ffb97cd1a8f3e98d023f0df05 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py @@ -0,0 +1,33 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.2-I2V-A14B_high_noise_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +state_dict = load_state_dict("models/train/Wan2.2-I2V-A14B_low_noise_full/epoch-1.safetensors") +pipe.dit2.load_state_dict(state_dict) + +input_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=input_image, + num_frames=49, + seed=1, tiled=True, +) +save_video(video, "video_Wan2.2-I2V-A14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-S2V-14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-S2V-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..0441a5459d31d46f3df958776440dd7ff69fcdf2 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-S2V-14B.py @@ -0,0 +1,53 @@ +import torch +from PIL import Image +import librosa +from diffsynth.utils.data import VideoData, save_video_with_audio +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/model.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], + audio_processor_config=ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/"), +) + +state_dict = load_state_dict("models/train/Wan2.2-S2V-14B_full/epoch-0.safetensors") +pipe.dit.load_state_dict(state_dict, strict=False) + + +num_frames = 81 # 4n+1 +height = 448 +width = 832 + +prompt = "a person is singing" +negative_prompt = "画面模糊，最差质量，画面模糊，细节模糊不清，情绪激动剧烈，手快速抖动，字幕，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" +input_image = Image.open("data/example_video_dataset/wans2v/pose.png").convert("RGB").resize((width, height)) +# s2v audio input, recommend 16kHz sampling rate +audio_path = 'data/example_video_dataset/wans2v/sing.MP3' +input_audio, sample_rate = librosa.load(audio_path, sr=16000) +# S2V pose video input +pose_video_path = 'data/example_video_dataset/wans2v/pose.mp4' +pose_video = VideoData(pose_video_path, height=height, width=width) + +# Speech-to-video with pose +video = pipe( + prompt=prompt, + input_image=input_image, + negative_prompt=negative_prompt, + seed=0, + num_frames=num_frames, + height=height, + width=width, + audio_sample_rate=sample_rate, + input_audio=input_audio, + s2v_pose_video=pose_video, + num_inference_steps=40, +) +save_video_with_audio(video[1:], "video_Wan2.2-S2V-14B.mp4", audio_path, fps=16, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py new file mode 100644 index 0000000000000000000000000000000000000000..34453b89115ac6ceba1c46aa398c7dcb46b3f32a --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py @@ -0,0 +1,28 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.2-T2V-A14B_high_noise_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +state_dict = load_state_dict("models/train/Wan2.2-T2V-A14B_low_noise_full/epoch-1.safetensors") +pipe.dit2.load_state_dict(state_dict) + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + seed=1, tiled=True +) +save_video(video, "video_Wan2.2-T2V-A14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py new file mode 100644 index 0000000000000000000000000000000000000000..75e108fed110a4e700c383fe5c0227a3ef776eab --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py @@ -0,0 +1,30 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-TI2V-5B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.2-TI2V-5B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-TI2V-5B", origin_file_pattern="Wan2.2_VAE.pth"), + ], +) +state_dict = load_state_dict("models/train/Wan2.2-TI2V-5B_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +input_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=input_image, + num_frames=49, + seed=1, tiled=True, +) +save_video(video, "video_Wan2.2-TI2V-5B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-VACE-Fun-A14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-VACE-Fun-A14B.py new file mode 100644 index 0000000000000000000000000000000000000000..38aa34cf5f0269fae377fb72b803c3f32b477caf --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/Wan2.2-VACE-Fun-A14B.py @@ -0,0 +1,43 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +vram_config = { + "offload_dtype": torch.bfloat16, + "offload_device": "cpu", + "onload_dtype": torch.bfloat16, + "onload_device": "cpu", + "preparing_dtype": torch.bfloat16, + "preparing_device": "cuda", + "computation_dtype": torch.bfloat16, + "computation_device": "cuda", +} +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config), + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="Wan2.1_VAE.pth", **vram_config), + ], +) +state_dict = load_state_dict("models/train/Wan2.2-VACE-Fun-A14B_high_noise_full/epoch-1.safetensors", torch_dtype=torch.bfloat16, device="cpu") +pipe.vace.load_state_dict(state_dict) +state_dict = load_state_dict("models/train/Wan2.2-VACE-Fun-A14B_low_noise_full/epoch-1.safetensors", torch_dtype=torch.bfloat16, device="cpu") +pipe.vace2.load_state_dict(state_dict) + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(17)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + vace_video=video, vace_reference_image=reference_image, num_frames=17, + seed=1, tiled=True +) +save_video(video, "video_Wan2.2-VACE-A14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/krea-realtime-video.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/krea-realtime-video.py new file mode 100644 index 0000000000000000000000000000000000000000..660ac70671521bf08ecb458454409b3dfa941f2e --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_full/krea-realtime-video.py @@ -0,0 +1,28 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="krea/krea-realtime-video", origin_file_pattern="krea-realtime-video-14b.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +state_dict = load_state_dict("models/train/krea-realtime-video_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +# Text-to-video +video = pipe( + prompt="a cat sitting on a boat", + num_inference_steps=6, num_frames=81, + seed=0, tiled=True, + cfg_scale=1, + sigma_shift=20, +) +save_video(video, "video_krea-realtime-video.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/LongCat-Video.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/LongCat-Video.py new file mode 100644 index 0000000000000000000000000000000000000000..bf5b5dcffdd82054f42d58ff4a7f4fbd0d74781a --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/LongCat-Video.py @@ -0,0 +1,23 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="meituan-longcat/LongCat-Video", origin_file_pattern="dit/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/LongCat-Video_lora/epoch-4.safetensors", alpha=1) + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + seed=1, tiled=True +) +save_video(video, "video_LongCat-Video.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Video-As-Prompt-Wan2.1-14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Video-As-Prompt-Wan2.1-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..ee962fcba557c26da658b46d79430b1c5c982087 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Video-As-Prompt-Wan2.1-14B.py @@ -0,0 +1,42 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="ByteDance/Video-As-Prompt-Wan2.1-14B", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Video-As-Prompt-Wan2.1-14B_lora/epoch-4.safetensors", alpha=1) + +ref_video_path = 'data/example_video_dataset/wanvap/vap_ref.mp4' +target_image_path = 'data/example_video_dataset/wanvap/input_image.jpg' + +image = Image.open(target_image_path).convert("RGB") +ref_video = VideoData(ref_video_path, height=480, width=832) +ref_frames = [ref_video[i] for i in range(49)] + +vap_prompt = "A man stands with his back to the camera on a dirt path overlooking sun-drenched, rolling green tea plantations. He wears a blue and green plaid shirt, dark pants, and white shoes. As he turns to face the camera and spreads his arms, a brief, magical burst of sparkling golden light particles envelops him. Through this shimmer, he seamlessly transforms into a Labubu toy character. His head morphs into the iconic large, furry-eared head of the toy, featuring a wide grin with pointed teeth and red cheek markings. The character retains the man's original plaid shirt and clothing, which now fit its stylized, cartoonish body. The camera remains static throughout the transformation, positioned low among the tea bushes, maintaining a consistent view of the subject and the expansive scenery." +prompt = "A young woman with curly hair, wearing a green hijab and a floral dress, plays a violin in front of a vintage green car on a tree-lined street. She executes a swift counter-clockwise turn to face the camera. During the turn, a brilliant shower of golden, sparkling particles erupts and momentarily obscures her figure. As the particles fade, she is revealed to have seamlessly transformed into a Labubu toy character. This new figure, now with the toy's signature large ears, big eyes, and toothy grin, maintains the original pose and continues playing the violin. The character's clothing—the green hijab, floral dress, and black overcoat—remains identical to the woman's. Throughout this transition, the camera stays static, and the street-side environment remains completely consistent." +negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards" + +video = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + input_image=image, + seed=42, tiled=True, + height=480, width=832, + num_frames=49, + vap_video=ref_frames, + vap_prompt=vap_prompt, + negative_vap_prompt=negative_prompt, +) +save_video(video, "video_Video-As-Prompt-Wan2.1-14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan-Dancer-14B-global.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan-Dancer-14B-global.py new file mode 100644 index 0000000000000000000000000000000000000000..4c1eb9b0230ea57a4cbfb32455af4f5fbd414319 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan-Dancer-14B-global.py @@ -0,0 +1,49 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="global_model.safetensors"), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) +pipe.load_lora(pipe.dit, "models/train/Wan-Dancer-14B-global_lora/epoch-4.safetensors", alpha=1) +dataset_snapshot_download( + "DiffSynth-Studio/diffsynth_example_dataset", + local_dir="data/diffsynth_example_dataset", + allow_file_pattern="wanvideo/Wan-Dancer-14B-global/*" +) +# This is a specialized model with the following constraints on its input parameters: +# * The model outputs a sequence of keyframes rather than a video; therefore, `framewise_decoding=True` must be set. +# * When the number of keyframes is $n$, `num_frames` = 4 * (n - 1) + 1. +# * Reducing `height`, `width`, `num_frames`, or `num_inference_steps` may lead to severe artifacts or generation failure. +# * The audio file specified by `wantodance_music_path` must match the video duration, calculated as (`num_frames` / 7.5) seconds. +# * The width and height of `wantodance_reference_image` must be multiples of 16. +# * `wantodance_fps` is configurable, but since the model appears to have been trained exclusively at 7.5 FPS, setting it to other values is not recommended. +# * The first frame of `wantodance_keyframes` is the `wantodance_reference_image`, while all subsequent frames are solid black. +# * `wantodance_keyframes_mask` indicates the positions of valid frames within `wantodance_keyframes`. +wantodance_keyframes = VideoData("data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-global/keyframes.mp4") +wantodance_keyframes = [wantodance_keyframes[i] for i in range(149)] +video = pipe( + prompt="一个人正在跳舞，舞蹈种类是韩舞。帧率是7.5000", + 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", + seed=0, tiled=False, + height=1280, width=720, num_frames=149, + num_inference_steps=48, + wantodance_music_path="data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-global/music.WAV", + wantodance_reference_image=Image.open("data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-global/refimage.jpg"), + wantodance_fps=7.5, + wantodance_keyframes=wantodance_keyframes, + wantodance_keyframes_mask=[1] + [0] * 148, + framewise_decoding=True, +) +save_video(video, "video_Wan-Dancer-14B-global.mp4", fps=7.5, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan-Dancer-14B-local.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan-Dancer-14B-local.py new file mode 100644 index 0000000000000000000000000000000000000000..d883f55087e7aebc0d80798ba410ea3dbd9174c7 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan-Dancer-14B-local.py @@ -0,0 +1,53 @@ +import torch, os +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="local_model.safetensors"), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan-Dancer-14B", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], + tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"), +) +pipe.load_lora(pipe.dit, "models/train/Wan-Dancer-14B-local_lora/epoch-4.safetensors", alpha=1) +dataset_snapshot_download( + "DiffSynth-Studio/diffsynth_example_dataset", + local_dir="data/diffsynth_example_dataset", + allow_file_pattern="wanvideo/Wan-Dancer-14B-local/*" +) +# This is a specialized model with the following constraints on its input parameters: +# * The model renders and outputs video based on a sequence of keyframes; therefore, `wantodance_keyframes` must be provided correctly. +# * If you need to generate a long video, please generate it in segments, and ensure that `wantodance_music_path`, `wantodance_keyframes`, and `wantodance_keyframes_mask` are properly split accordingly. +# * The audio file specified by `wantodance_music_path` must match the video duration, calculated as (`num_frames` / 30) seconds. +# * The width and height of `wantodance_reference_image` must be multiples of 16. +# * `wantodance_fps` is configurable, but since the model appears to have been trained exclusively at 30 FPS, setting it to other values is not recommended. +# * In `wantodance_keyframes`, frames that are not keyframes should be solid black. +# * `wantodance_keyframes_mask` indicates the positions of valid frames within `wantodance_keyframes`. +wantodance_keyframes = VideoData("data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-local/keyframes.mp4") +wantodance_keyframes = [wantodance_keyframes[i] for i in range(149)] +video = pipe( + prompt="一个人正在跳舞，舞蹈种类是古典舞,图像清晰程度高,人物动作平均幅度中等,人物动作最大幅度中等。, 帧率是30fps。", + 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", + seed=0, tiled=True, + height=1280, width=720, num_frames=149, + num_inference_steps=24, + wantodance_music_path="data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-local/music.wav", + wantodance_reference_image=Image.open("data/diffsynth_example_dataset/wanvideo/Wan-Dancer-14B-local/refimage.jpg"), + wantodance_fps=30, + wantodance_keyframes=wantodance_keyframes, + wantodance_keyframes_mask=[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1], +) +save_video(video, "video_Wan-Dancer-14B-local.mp4", fps=30, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py new file mode 100644 index 0000000000000000000000000000000000000000..4137b4947598f10a943f8f673b955a4140135c7e --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py @@ -0,0 +1,27 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1", origin_file_pattern="model.safetensors"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-1.3b-speedcontrol-v1_lora/epoch-4.safetensors", alpha=1) + +# Text-to-video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + seed=1, tiled=True, + motion_bucket_id=50 +) +save_video(video, "video_Wan2.1-1.3b-speedcontrol-v1.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py new file mode 100644 index 0000000000000000000000000000000000000000..62f8c0ce66099e292ea121d17514ca1d19b0758c --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-FLF2V-14B-720P_lora/epoch-4.safetensors", alpha=1) + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=video[0], + end_image=video[80], + seed=0, tiled=True, + sigma_shift=16, +) +save_video(video, "video_Wan2.1-FLF2V-14B-720P.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..dc7f9dc5aadb4c0252f6608e5658305d2f2d9b26 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py @@ -0,0 +1,30 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-1.3B-Control_lora/epoch-4.safetensors", alpha=1) + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(81)] + +# Control video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + control_video=video, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-1.3B-Control.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..c4134a0a15d038e27f97f0727b836e807faad56f --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py @@ -0,0 +1,29 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-1.3B-InP_lora/epoch-4.safetensors", alpha=1) + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=video[0], end_image=video[80], + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-1.3B-InP.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..1dfec523a485812107bd24446b951b896e219522 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py @@ -0,0 +1,30 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-14B-Control_lora/epoch-4.safetensors", alpha=1) + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(81)] + +# Control video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + control_video=video, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-14B-Control.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..92b90a9436a34f11a8a8f20a7de61850df8023d9 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py @@ -0,0 +1,29 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-14B-InP_lora/epoch-4.safetensors", alpha=1) + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=video[0], end_image=video[80], + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-14B-InP.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..d3c44ed8e63541da713929995ee748261ebec100 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-V1.1-1.3B-Control-Camera_lora/epoch-4.safetensors", alpha=1) + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=video[0], + camera_control_direction="Left", camera_control_speed=0.0, + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-1.3B-Control-Camera.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..2a113e747e2613e2fba63369c6982e986befa213 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-V1.1-1.3B-Control_lora/epoch-4.safetensors", alpha=1) + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(81)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +# Control video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + control_video=video, reference_image=reference_image, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-1.3B-Control.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..8bca71aa8f6499585076eb15df9263f2dac7a5ab --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py @@ -0,0 +1,29 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-V1.1-1.3B-InP_lora/epoch-4.safetensors", alpha=1) + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=video[0], end_image=video[80], + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-1.3B-InP.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..ea28432f65d97a4a3910dc8414e0e7d327660114 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-V1.1-14B-Control-Camera_lora/epoch-4.safetensors", alpha=1) + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=video[0], + camera_control_direction="Left", camera_control_speed=0.0, + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-14B-Control-Camera.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..fae3f8c7dd31ca7f8722f3227dd7d222a82da838 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-V1.1-14B-Control_lora/epoch-4.safetensors", alpha=1) + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(81)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +# Control video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + control_video=video, reference_image=reference_image, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-14B-Control.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..6c3298d3e115fcfea978196e88160f9790ea6765 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py @@ -0,0 +1,29 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-V1.1-14B-InP_lora/epoch-4.safetensors", alpha=1) + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=video[0], end_image=video[80], + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-14B-InP.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2d149ae77452c971ae558d611c9360d4bab864 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py @@ -0,0 +1,28 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-I2V-14B-480P_lora/epoch-4.safetensors", alpha=1) + +input_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=input_image, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-I2V-14B-480P.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py new file mode 100644 index 0000000000000000000000000000000000000000..aa52055b79939a73f32c33bc3f8e1af86686cab7 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py @@ -0,0 +1,29 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-I2V-14B-720P_lora/epoch-4.safetensors", alpha=1) + +input_image = VideoData("data/example_video_dataset/video1.mp4", height=720, width=1280)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=input_image, + height=720, width=1280, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-I2V-14B-720P.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..8d316b5bada301d0b08a55cbaee085fcd42e92e6 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py @@ -0,0 +1,23 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-T2V-1.3B_lora/epoch-4.safetensors", alpha=1) + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-T2V-1.3B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..4b2a44581d7b828025fa9e041640a922d0994a68 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py @@ -0,0 +1,23 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-T2V-14B_lora/epoch-4.safetensors", alpha=1) + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-T2V-14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py new file mode 100644 index 0000000000000000000000000000000000000000..7a401d8d75d23f635b72af3d8f90d33844f724fa --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py @@ -0,0 +1,28 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +pipe.load_lora(pipe.vace, "models/train/Wan2.1-VACE-1.3B-Preview_lora/epoch-4.safetensors", alpha=1) + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(49)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + vace_video=video, vace_reference_image=reference_image, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-VACE-1.3B-Preview.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..fe5e19b541143f3c1487f5220ae34e369916790f --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py @@ -0,0 +1,28 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +pipe.load_lora(pipe.vace, "models/train/Wan2.1-VACE-1.3B_lora/epoch-4.safetensors", alpha=1) + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(49)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + vace_video=video, vace_reference_image=reference_image, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-VACE-1.3B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..5bbe9454e75c187a56d92f18e26c914046f7c78b --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py @@ -0,0 +1,28 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +pipe.load_lora(pipe.vace, "models/train/Wan2.1-VACE-14B_lora/epoch-4.safetensors", alpha=1) + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(17)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + vace_video=video, vace_reference_image=reference_image, num_frames=17, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-VACE-14B.mp4", fps=15, quality=5) diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.2-Animate-14B.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.2-Animate-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..79326cd08e93c846f4b9a97710aea66b9ade07cb --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.2-Animate-14B.py @@ -0,0 +1,32 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.2-Animate-14B_lora/epoch-4.safetensors", alpha=1) + +input_image = VideoData("data/example_video_dataset/animate/animate_output.mp4", height=480, width=832)[0] +animate_pose_video = VideoData("data/examples/wan/animate/animate_pose_video.mp4", height=480, width=832).raw_data()[:81-4] +animate_face_video = VideoData("data/examples/wan/animate/animate_face_video.mp4", height=512, width=512).raw_data()[:81-4] +video = pipe( + prompt="视频中的人在做动作", + seed=0, tiled=True, + input_image=input_image, + animate_pose_video=animate_pose_video, + animate_face_video=animate_face_video, + num_frames=81, height=480, width=832, + num_inference_steps=20, cfg_scale=1, +) +save_video(video, "video_Wan2.2-Animate-14B.mp4", fps=15, quality=5) \ No newline at end of file diff --git a/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..d1967f7c8ecf8c2d988970d0896db054641e7513 --- /dev/null +++ b/video_gen_14d/third_party/DiffSynth-Studio/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py @@ -0,0 +1,32 @@ +import torch +from PIL import Image +from diffsynth.utils.data import save_video, VideoData +from diffsynth.core import load_state_dict +from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.2-Fun-A14B-Control-Camera_high_noise_lora/epoch-4.safetensors", alpha=1) +pipe.load_lora(pipe.dit2, "models/train/Wan2.2-Fun-A14B-Control-Camera_low_noise_lora/epoch-4.safetensors", alpha=1) + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + 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", + input_image=video[0], + camera_control_direction="Left", camera_control_speed=0.0, + seed=0, tiled=True +) +save_video(video, "video_Wan2.2-Fun-A14B-Control-Camera.mp4", fps=15, quality=5)