Spaces:
Running
on
Zero
Running
on
Zero
Increase duration
Browse files
app.py
CHANGED
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@@ -8,9 +8,8 @@ from pikigen import PikigenPipeline
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# Trick required because it is not a native diffusers model
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from diffusers.pipelines.pipeline_loading_utils import LOADABLE_CLASSES, ALL_IMPORTABLE_CLASSES
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LOADABLE_CLASSES
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ALL_IMPORTABLE_CLASSES.setdefault("DiT", []).extend(["save_pretrained", "from_pretrained"])
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model_repo_id = "Freepik/Pikigen-test"
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@@ -22,6 +21,7 @@ else:
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pipe = PikigenPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
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# pipe.enable_model_cpu_offload() # For less memory consumption
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pipe.vae.enable_slicing()
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pipe.vae.enable_tiling()
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@@ -29,7 +29,7 @@ MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 1024
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@spaces.GPU
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def infer(
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prompt,
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negative_prompt,
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# Trick required because it is not a native diffusers model
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from diffusers.pipelines.pipeline_loading_utils import LOADABLE_CLASSES, ALL_IMPORTABLE_CLASSES
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LOADABLE_CLASSES["pikigen"] = LOADABLE_CLASSES["pikigen.model"] = {"DiT": ["save_pretrained", "from_pretrained"]}
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ALL_IMPORTABLE_CLASSES["DiT"] = ["save_pretrained", "from_pretrained"]
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model_repo_id = "Freepik/Pikigen-test"
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pipe = PikigenPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
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# pipe.enable_model_cpu_offload() # For less memory consumption
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pipe.to(device)
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pipe.vae.enable_slicing()
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pipe.vae.enable_tiling()
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MAX_IMAGE_SIZE = 1024
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@spaces.GPU(duration=500)
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def infer(
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prompt,
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negative_prompt,
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