Model URI doesn't work
#4
by
Trotter
- opened
README.md
CHANGED
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@@ -16,8 +16,8 @@ tags:
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| Model | Params |
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|-------|--------|
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| [amused-256](https://huggingface.co/
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| [amused-512](https://huggingface.co/
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Amused is a lightweight text to image model based off of the [muse](https://arxiv.org/pdf/2301.00704.pdf) architecture. Amused is particularly useful in applications that require a lightweight and fast model such as generating many images quickly at once.
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@@ -34,7 +34,7 @@ import torch
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from diffusers import AmusedPipeline
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pipe = AmusedPipeline.from_pretrained(
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"
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)
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pipe.vqvae.to(torch.float32) # vqvae is producing nans in fp16
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pipe = pipe.to("cuda")
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@@ -53,7 +53,7 @@ import torch
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from diffusers import AmusedPipeline
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pipe = AmusedPipeline.from_pretrained(
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)
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pipe.vqvae.to(torch.float32) # vqvae is producing nans n fp16
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pipe = pipe.to("cuda")
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@@ -103,7 +103,7 @@ from diffusers import AmusedImg2ImgPipeline
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from diffusers.utils import load_image
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pipe = AmusedImg2ImgPipeline.from_pretrained(
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)
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pipe.vqvae.to(torch.float32) # vqvae is producing nans in fp16
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pipe = pipe.to("cuda")
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@@ -134,7 +134,7 @@ from diffusers.utils import load_image
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from PIL import Image
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pipe = AmusedInpaintPipeline.from_pretrained(
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"
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)
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pipe.vqvae.to(torch.float32) # vqvae is producing nans in fp16
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pipe = pipe.to("cuda")
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@@ -171,7 +171,7 @@ from diffusers import AmusedInpaintPipeline
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from diffusers.utils import load_image
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pipe = AmusedInpaintPipeline.from_pretrained(
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"
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)
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pipe.vqvae.to(torch.float32) # vqvae is producing nans in fp16
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pipe = pipe.to("cuda")
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@@ -239,7 +239,7 @@ import torch
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from diffusers import AmusedPipeline
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pipe = AmusedPipeline.from_pretrained(
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"
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)
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# HERE use torch.compile
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| Model | Params |
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|-------|--------|
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| [amused-256](https://huggingface.co/amused/amused-256) | 603M |
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| [amused-512](https://huggingface.co/amused/amused-512) | 608M |
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Amused is a lightweight text to image model based off of the [muse](https://arxiv.org/pdf/2301.00704.pdf) architecture. Amused is particularly useful in applications that require a lightweight and fast model such as generating many images quickly at once.
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from diffusers import AmusedPipeline
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pipe = AmusedPipeline.from_pretrained(
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"amused/amused-256", variant="fp16", torch_dtype=torch.float16
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)
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pipe.vqvae.to(torch.float32) # vqvae is producing nans in fp16
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pipe = pipe.to("cuda")
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from diffusers import AmusedPipeline
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pipe = AmusedPipeline.from_pretrained(
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"amused/amused-512", variant="fp16", torch_dtype=torch.float16
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)
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pipe.vqvae.to(torch.float32) # vqvae is producing nans n fp16
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pipe = pipe.to("cuda")
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from diffusers.utils import load_image
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pipe = AmusedImg2ImgPipeline.from_pretrained(
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"amused/amused-512", variant="fp16", torch_dtype=torch.float16
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)
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pipe.vqvae.to(torch.float32) # vqvae is producing nans in fp16
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pipe = pipe.to("cuda")
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from PIL import Image
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pipe = AmusedInpaintPipeline.from_pretrained(
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+
"amused/amused-256", variant="fp16", torch_dtype=torch.float16
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)
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pipe.vqvae.to(torch.float32) # vqvae is producing nans in fp16
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pipe = pipe.to("cuda")
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from diffusers.utils import load_image
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pipe = AmusedInpaintPipeline.from_pretrained(
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+
"amused/amused-512", variant="fp16", torch_dtype=torch.float16
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)
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pipe.vqvae.to(torch.float32) # vqvae is producing nans in fp16
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pipe = pipe.to("cuda")
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from diffusers import AmusedPipeline
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pipe = AmusedPipeline.from_pretrained(
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+
"amused/amused-256", variant="fp16", torch_dtype=torch.float16
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)
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# HERE use torch.compile
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