@@ -132,30 +132,36 @@ depth = model.infer_image(raw_img) # HxW raw depth map in numpy
132132const diffusers_default = ( model : ModelData ) => [
133133 `from diffusers import DiffusionPipeline
134134
135- pipeline = DiffusionPipeline.from_pretrained("${ model . id } ")` ,
135+ pipe = DiffusionPipeline.from_pretrained("${ model . id } ")
136+
137+ prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
138+ image = pipe(prompt).images[0]` ,
136139] ;
137140
138141const diffusers_controlnet = ( model : ModelData ) => [
139142 `from diffusers import ControlNetModel, StableDiffusionControlNetPipeline
140143
141144controlnet = ControlNetModel.from_pretrained("${ model . id } ")
142- pipeline = StableDiffusionControlNetPipeline.from_pretrained(
145+ pipe = StableDiffusionControlNetPipeline.from_pretrained(
143146 "${ get_base_diffusers_model ( model ) } ", controlnet=controlnet
144147)` ,
145148] ;
146149
147150const diffusers_lora = ( model : ModelData ) => [
148151 `from diffusers import DiffusionPipeline
149152
150- pipeline = DiffusionPipeline.from_pretrained("${ get_base_diffusers_model ( model ) } ")
151- pipeline.load_lora_weights("${ model . id } ")` ,
153+ pipe = DiffusionPipeline.from_pretrained("${ get_base_diffusers_model ( model ) } ")
154+ pipe.load_lora_weights("${ model . id } ")
155+
156+ prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
157+ image = pipe(prompt).images[0]` ,
152158] ;
153159
154160const diffusers_textual_inversion = ( model : ModelData ) => [
155161 `from diffusers import DiffusionPipeline
156162
157- pipeline = DiffusionPipeline.from_pretrained("${ get_base_diffusers_model ( model ) } ")
158- pipeline .load_textual_inversion("${ model . id } ")`,
163+ pipe = DiffusionPipeline.from_pretrained("${ get_base_diffusers_model ( model ) } ")
164+ pipe .load_textual_inversion("${ model . id } ")`,
159165] ;
160166
161167export const diffusers = ( model : ModelData ) : string [ ] => {
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