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load CLIP on training device

This commit is contained in:
Sean Sube 2023-02-21 22:12:12 -06:00
parent 6b4ced2608
commit 3f4b3fa322
Signed by: ssube
GPG Key ID: 3EED7B957D362AF1
2 changed files with 3 additions and 3 deletions
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2
api/onnx_web/convert/__main__.py
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@ -234,7 +234,7 @@ def convert_models(ctx: ConversionContext, args, models: Models):
inversion_name = inversion["name"]
inversion_source = inversion["source"]
inversion_source = fetch_model(ctx, f"{name}-inversion-{inversion_name}", inversion_source)
convert_diffusion_textual_inversion(ctx, inversion_name, source, inversion_source)
convert_diffusion_textual_inversion(ctx, inversion_name, model["source"], inversion_source)
except Exception as e:
logger.error("error converting diffusion model %s: %s", name, e)

4
api/onnx_web/convert/diffusion/textual_inversion.py
View File

@ -29,11 +29,11 @@ def convert_diffusion_textual_inversion(context: ConversionContext, name: str, b
tokenizer = CLIPTokenizer.from_pretrained(
base_model,
subfolder="tokenizer",
)
).to(context.training_device)
text_encoder = CLIPTextModel.from_pretrained(
base_model,
subfolder="text_encoder",
)
).to(context.training_device)
loaded_embeds = torch.load(embeds_file, map_location=context.map_location)