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Update diffusers default snippets to be complete. #907

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Merged
merged 6 commits into from
Sep 18, 2024
Merged

Update diffusers default snippets to be complete. #907

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d05764b
Update diffusers default snippets to be complete.
Vaibhavs10 Sep 17, 2024
84ededa
Lint.
Vaibhavs10 Sep 17, 2024
1d055b4
pnpm format
mishig25 Sep 17, 2024
57a4dac
Suggestions from review.
Vaibhavs10 Sep 17, 2024
5ea7abb
fkn lint.
Vaibhavs10 Sep 17, 2024
2bf47fa
Merge branch 'main' into vb/fix-diffusers-snippet
Vaibhavs10 Sep 18, 2024
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18 changes: 12 additions & 6 deletions packages/tasks/src/model-libraries-snippets.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -132,30 +132,36 @@ depth = model.infer_image(raw_img) # HxW raw depth map in numpy
const diffusers_default = (model: ModelData) => [
`from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained("${model.id}")`,
pipe = DiffusionPipeline.from_pretrained("${model.id}")

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]`,
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Nit: as discussed in Slack, having a prompt might be a bit confusing due to many models expecting certain tokens. In any case, this is fine I think to showcase usage

];

const diffusers_controlnet = (model: ModelData) => [
`from diffusers import ControlNetModel, StableDiffusionControlNetPipeline

controlnet = ControlNetModel.from_pretrained("${model.id}")
pipeline = StableDiffusionControlNetPipeline.from_pretrained(
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"${get_base_diffusers_model(model)}", controlnet=controlnet
)`,
];

const diffusers_lora = (model: ModelData) => [
`from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained("${get_base_diffusers_model(model)}")
pipeline.load_lora_weights("${model.id}")`,
pipe = DiffusionPipeline.from_pretrained("${get_base_diffusers_model(model)}")
pipe.load_lora_weights("${model.id}")

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]`,
];

const diffusers_textual_inversion = (model: ModelData) => [
`from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained("${get_base_diffusers_model(model)}")
pipeline.load_textual_inversion("${model.id}")`,
pipe = DiffusionPipeline.from_pretrained("${get_base_diffusers_model(model)}")
pipe.load_textual_inversion("${model.id}")`,
];

export const diffusers = (model: ModelData): string[] => {
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