Publish Structure Diffusion pipeline at f15077d69de8
Browse files
README.md
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@@ -94,7 +94,7 @@ Inspect generated images before downstream use, and record the checkpoint revisi
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Install the package from this repository archive and download the English parser models before the first inference:
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```bash
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pip install "structure-diffusion @ https://github.com/creative-graphic-design/design-generators/archive/
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python -c "import stanza; stanza.download('en', processors='tokenize,pos,constituency')"
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```
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@@ -116,7 +116,7 @@ pipe = DiffusionPipeline.from_pretrained(
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token=token,
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trust_remote_code=True,
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)
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pipe.to("
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result = pipe(
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"A red car and a white sheep",
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parser_type="constituency",
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@@ -210,7 +210,7 @@ baseline = StableDiffusionPipeline.from_pretrained(
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torch_dtype=torch.float32,
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token=token,
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)
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baseline.to("
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for slug, prompt in EXAMPLES:
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for seed in SEEDS:
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image = baseline(
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@@ -231,7 +231,7 @@ structured = StructureDiffusionPipeline.from_pretrained(
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torch_dtype=torch.float32,
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token=token,
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)
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structured.to("
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for slug, prompt in EXAMPLES:
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for seed in SEEDS:
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image = structured(
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Install the package from this repository archive and download the English parser models before the first inference:
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```bash
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pip install "structure-diffusion @ https://github.com/creative-graphic-design/design-generators/archive/f15077d69de8442d7e7021979895bdee751159f3.zip#subdirectory=models/structure-diffusion"
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python -c "import stanza; stanza.download('en', processors='tokenize,pos,constituency')"
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```
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token=token,
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trust_remote_code=True,
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)
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pipe.to("cuda")
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result = pipe(
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"A red car and a white sheep",
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parser_type="constituency",
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torch_dtype=torch.float32,
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token=token,
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)
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baseline.to("cuda")
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for slug, prompt in EXAMPLES:
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for seed in SEEDS:
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image = baseline(
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torch_dtype=torch.float32,
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token=token,
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)
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structured.to("cuda")
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for slug, prompt in EXAMPLES:
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for seed in SEEDS:
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image = structured(
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