Update README.md

README.md

@@ -36,15 +36,12 @@ from diffusers.utils import load_image
 blip_diffusion_pipe= BlipDiffusionPipeline.from_pretrained('ayushtues/blipdiffusion')
 blip_diffusion_pipe.to('cuda')
 
-cond_subject = ["dog"]
-tgt_subject = ["dog"]
-text_prompt_input = ["swimming underwater"]
+cond_subject = "dog"
+tgt_subject = "dog"
+text_prompt_input = "swimming underwater"
 
 
 cond_image = load_image("https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/dog.jpg")
-num_output = 1
-
-iter_seed = 88888
 guidance_scale = 7.5
 num_inference_steps = 50
 negative_prompt = "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate"
@@ -76,18 +73,15 @@ from controlnet_aux import CannyDetector
 blip_diffusion_pipe= BlipDiffusionControlNetPipeline.from_pretrained("ayushtues/blipdiffusion-controlnet")
 blip_diffusion_pipe.to('cuda')
 
-style_subject = ["flower"] # subject that defines the style
-tgt_subject = ["teapot"]  # subject to generate.
-text_prompt = ["on a marble table"]
+style_subject = "flower" # subject that defines the style
+tgt_subject = "teapot"  # subject to generate.
+text_prompt = "on a marble table"
 cldm_cond_image = load_image("https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/kettle.jpg").resize((512, 512))
 canny = CannyDetector()
 cldm_cond_image = canny(cldm_cond_image, 30, 70, output_type='pil')
-cldm_cond_image = [cldm_cond_image ]
 
 style_image = load_image("https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg")
 
-
-num_output = 1
 guidance_scale = 7.5
 num_inference_steps = 50
 negative_prompt = "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate"
@@ -122,19 +116,15 @@ controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble"
 blip_diffusion_pipe.controlnet = controlnet
 blip_diffusion_pipe.to('cuda')
 
-style_subject = ["flower"] # subject that defines the style
-tgt_subject = ["bag"]  # subject to generate.
-text_prompt = ["on a table"]
+style_subject = "flower" # subject that defines the style
+tgt_subject = "bag"  # subject to generate.
+text_prompt = "on a table"
 cldm_cond_image = load_image("https://huggingface.co/lllyasviel/sd-controlnet-scribble/resolve/main/images/bag.png" ).resize((512, 512))
 hed = HEDdetector.from_pretrained("lllyasviel/Annotators")
 cldm_cond_image = hed(cldm_cond_image)
-cldm_cond_image = [cldm_cond_image ]
 
 style_image = load_image("https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg")
 
-
-num_output = 1
-iter_seed = 88888
 guidance_scale = 7.5
 num_inference_steps = 50
 negative_prompt = "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate"