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Update app.py
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app.py
CHANGED
@@ -61,10 +61,11 @@ transformer = SD3Transformer2DModel.from_pretrained(
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model_path, subfolder="transformer", torch_dtype=torch.bfloat16
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)
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pipe = StableDiffusion3Pipeline.from_pretrained(
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#"stabilityai # stable-diffusion-3.5-large",
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"ford442/stable-diffusion-3.5-large-bf16",
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# vae=AutoencoderKL.from_pretrained("ford442/stable-diffusion-3.5-large-fp32", use_safetensors=True, subfolder='vae',token=True),
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#scheduler = FlowMatchHeunDiscreteScheduler.from_pretrained('ford442/stable-diffusion-3.5-large-bf16', subfolder='scheduler',token=True),
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text_encoder=None, #CLIPTextModelWithProjection.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder', token=True),
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text_encoder_2=None, #CLIPTextModelWithProjection.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder_2',token=True),
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@@ -74,13 +75,14 @@ pipe = StableDiffusion3Pipeline.from_pretrained(
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tokenizer_3=T5TokenizerFast.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", use_fast=True, subfolder="tokenizer_3", token=True),
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torch_dtype=torch.bfloat16,
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transformer=transformer,
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#use_safetensors=False,
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)
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#pipe.to(device=device, dtype=torch.bfloat16)
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pipe.to(device)
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text_encoder=CLIPTextModelWithProjection.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder', token=True).to(device=device, dtype=torch.bfloat16)
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text_encoder_2=CLIPTextModelWithProjection.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder_2',token=True).to(device=device, dtype=torch.bfloat16)
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text_encoder_3=T5EncoderModel.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder_3',token=True).to(device=device, dtype=torch.bfloat16)
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model_path, subfolder="transformer", torch_dtype=torch.bfloat16
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)
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vaeX=AutoencoderKL.from_pretrained("ford442/stable-diffusion-3.5-large-fp32", safety_checker=None, use_safetensors=True, low_cpu_mem_usage=False, subfolder='vae', torch_dtype=torch.float32, token=True)
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pipe = StableDiffusion3Pipeline.from_pretrained(
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#"stabilityai # stable-diffusion-3.5-large",
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"ford442/stable-diffusion-3.5-large-bf16",
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#scheduler = FlowMatchHeunDiscreteScheduler.from_pretrained('ford442/stable-diffusion-3.5-large-bf16', subfolder='scheduler',token=True),
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text_encoder=None, #CLIPTextModelWithProjection.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder', token=True),
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text_encoder_2=None, #CLIPTextModelWithProjection.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder_2',token=True),
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tokenizer_3=T5TokenizerFast.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", use_fast=True, subfolder="tokenizer_3", token=True),
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torch_dtype=torch.bfloat16,
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transformer=transformer,
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vae=None
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#use_safetensors=False,
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)
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#pipe.to(device=device, dtype=torch.bfloat16)
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pipe.to(device)
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pipe.vae=vaeX.to(device)
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text_encoder=CLIPTextModelWithProjection.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder', token=True).to(device=device, dtype=torch.bfloat16)
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text_encoder_2=CLIPTextModelWithProjection.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder_2',token=True).to(device=device, dtype=torch.bfloat16)
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text_encoder_3=T5EncoderModel.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder_3',token=True).to(device=device, dtype=torch.bfloat16)
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