bug fix
Browse files
app.py
CHANGED
@@ -69,7 +69,7 @@ def get_modelscope_pipeline(
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lora.merge_and_unload()
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pipe.unet = lora
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return pipe
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@@ -136,7 +136,7 @@ def get_animatediff_pipeline(
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lora.merge_and_unload()
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pipe.unet = lora
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return pipe
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@@ -146,42 +146,42 @@ pipe_dict = {
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"AnimateDiff (RealisticVision)": {"WebVid": None, "LAION-aes": None},
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"AnimateDiff (epiCRealism)": {"WebVid": None, "LAION-aes": None},
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}
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def init_pipelines():
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@spaces.GPU(duration=120)
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def infer(
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base_model, variant, prompt, num_inference_steps=4, seed=0, randomize_seed=True,
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):
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# if pipe_dict[base_model][variant] is None:
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# if base_model == "ModelScope T2V":
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@@ -206,46 +206,47 @@ def infer(
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# )
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# else:
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# raise ValueError(f"Unknown base_model {base_model}")
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pipe_dict[base_model][variant] = pipe_dict[base_model][variant].to(device)
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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generator = torch.Generator("cpu").manual_seed(seed)
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prompt=prompt,
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num_frames=16,
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guidance_scale=1.0,
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@@ -265,7 +266,7 @@ def infer(
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fps=7,
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)
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print(f"Saved to {save_path}")
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pipe_dict[base_model][variant] = pipe_dict[base_model][variant].to("cpu")
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return save_path, seed
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@@ -276,7 +277,7 @@ examples = [
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"Aerial uhd 4k view. mid-air flight over fresh and clean mountain river at sunny summer morning. Green trees and sun rays on horizon. Direct on sun.",
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4
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],
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["ModelScope T2V", "Anime", "Timelapse misty mountain landscape"],
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[
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"ModelScope T2V",
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"WebVid",
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@@ -338,7 +339,7 @@ variants = {
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def update_variant(rs):
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return gr.update(choices=variants[rs], value=None)
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init_pipelines()
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with gr.Blocks(css=css) as demo:
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lora.merge_and_unload()
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pipe.unet = lora
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pipe = pipe.to(device)
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return pipe
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lora.merge_and_unload()
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pipe.unet = lora
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pipe = pipe.to(device)
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return pipe
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"AnimateDiff (RealisticVision)": {"WebVid": None, "LAION-aes": None},
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"AnimateDiff (epiCRealism)": {"WebVid": None, "LAION-aes": None},
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}
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cache_pipeline = {
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"base_model": None,
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"variant": None,
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"pipeline": None,
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}
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# def init_pipelines():
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# for base_model in variants.keys():
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# for variant in variants[base_model]:
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# if pipe_dict[base_model][variant] is None:
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# if base_model == "ModelScope T2V":
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# pipe_dict[base_model][variant] = get_modelscope_pipeline(mcm_variant=variant)
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# elif base_model == "AnimateDiff (SD1.5)":
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# pipe_dict[base_model][variant] = get_animatediff_pipeline(
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# real_variant=None,
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# motion_module_path="guoyww/animatediff-motion-adapter-v1-5-2",
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# mcm_variant=variant,
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# )
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# elif base_model == "AnimateDiff (RealisticVision)":
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# pipe_dict[base_model][variant] = get_animatediff_pipeline(
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# real_variant="realvision",
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# motion_module_path="guoyww/animatediff-motion-adapter-v1-5-2",
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# mcm_variant=variant,
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# )
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# elif base_model == "AnimateDiff (epiCRealism)":
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# pipe_dict[base_model][variant] = get_animatediff_pipeline(
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# real_variant="epicrealism",
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# motion_module_path="guoyww/animatediff-motion-adapter-v1-5-2",
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# mcm_variant=variant,
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# )
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# else:
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# raise ValueError(f"Unknown base_model {base_model}")
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@spaces.GPU(duration=120)
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def infer(
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base_model, variant, prompt, num_inference_steps=4, seed=0, randomize_seed=True,
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):
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# if pipe_dict[base_model][variant] is None:
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# if base_model == "ModelScope T2V":
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# )
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# else:
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# raise ValueError(f"Unknown base_model {base_model}")
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if (
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cache_pipeline["base_model"] == base_model
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and cache_pipeline["variant"] == variant
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):
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pass
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else:
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if base_model == "ModelScope T2V":
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pipeline = get_modelscope_pipeline(mcm_variant=variant)
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elif base_model == "AnimateDiff (SD1.5)":
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pipeline = get_animatediff_pipeline(
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real_variant=None,
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motion_module_path="guoyww/animatediff-motion-adapter-v1-5-2",
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mcm_variant=variant,
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)
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elif base_model == "AnimateDiff (RealisticVision)":
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pipeline = get_animatediff_pipeline(
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real_variant="realvision",
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motion_module_path="guoyww/animatediff-motion-adapter-v1-5-2",
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mcm_variant=variant,
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)
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elif base_model == "AnimateDiff (epiCRealism)":
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pipeline = get_animatediff_pipeline(
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real_variant="epicrealism",
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motion_module_path="guoyww/animatediff-motion-adapter-v1-5-2",
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mcm_variant=variant,
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)
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else:
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raise ValueError(f"Unknown base_model {base_model}")
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cache_pipeline["base_model"] = base_model
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cache_pipeline["variant"] = variant
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cache_pipeline["pipeline"] = pipeline
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# pipe_dict[base_model][variant] = pipe_dict[base_model][variant].to(device)
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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generator = torch.Generator("cpu").manual_seed(seed)
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progress=gr.Progress(track_tqdm=True)
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output = cache_pipeline["pipeline"](
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prompt=prompt,
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num_frames=16,
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guidance_scale=1.0,
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fps=7,
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)
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print(f"Saved to {save_path}")
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# pipe_dict[base_model][variant] = pipe_dict[base_model][variant].to("cpu")
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return save_path, seed
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"Aerial uhd 4k view. mid-air flight over fresh and clean mountain river at sunny summer morning. Green trees and sun rays on horizon. Direct on sun.",
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4
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],
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["ModelScope T2V", "Anime", "Timelapse misty mountain landscape", 4],
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[
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"ModelScope T2V",
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"WebVid",
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def update_variant(rs):
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return gr.update(choices=variants[rs], value=None)
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# init_pipelines()
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with gr.Blocks(css=css) as demo:
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