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Update app.py
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app.py
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@@ -6,9 +6,16 @@ import json
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import torch
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import spaces
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from diffusers import AutoencoderKL, SD3Transformer2DModel, StableDiffusion3Pipeline
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from diffusers.loaders.single_file_utils import convert_sd3_transformer_checkpoint_to_diffusers
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from huggingface_hub import hf_hub_download
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from transformers import (
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CLIPTextModelWithProjection,
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CLIPTokenizer,
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@@ -57,7 +64,7 @@ text_encoder_3 = T5EncoderModel.from_pretrained(model_repo_id, subfolder="text_e
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tokenizer = CLIPTokenizer.from_pretrained(model_repo_id, subfolder="tokenizer")
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tokenizer_2 = CLIPTokenizer.from_pretrained(model_repo_id, subfolder="tokenizer_2")
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tokenizer_3 = T5Tokenizer.from_pretrained(model_repo_id, subfolder="tokenizer_3")
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# Create pipeline from our models
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pipe = StableDiffusion3Pipeline(
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import torch
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import spaces
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from huggingface_hub import hf_hub_download
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from diffusers import (
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AutoencoderKL,
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SD3Transformer2DModel,
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StableDiffusion3Pipeline,
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FlowMatchEulerDiscreteScheduler
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)
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from diffusers.loaders.single_file_utils import (
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convert_sd3_transformer_checkpoint_to_diffusers,
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)
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from transformers import (
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CLIPTextModelWithProjection,
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CLIPTokenizer,
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tokenizer = CLIPTokenizer.from_pretrained(model_repo_id, subfolder="tokenizer")
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tokenizer_2 = CLIPTokenizer.from_pretrained(model_repo_id, subfolder="tokenizer_2")
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tokenizer_3 = T5Tokenizer.from_pretrained(model_repo_id, subfolder="tokenizer_3")
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scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(model_repo_id, subfolder="scheduler")
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# Create pipeline from our models
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pipe = StableDiffusion3Pipeline(
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