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update gradio
Browse files- app.py +8 -8
- requirements.txt +1 -1
app.py
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@@ -1,6 +1,5 @@
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from types import MethodType
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import spaces
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import os
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import gradio as gr
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import torch
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@@ -14,7 +13,7 @@ from huggingface_hub import snapshot_download
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from PIL import Image
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from ip_adapter import StyleShot, StyleContentStableDiffusionControlNetPipeline
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device = "cuda"
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contour_detector = SOFT_HEDdetector()
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lineart_detector = LineartDetector()
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@@ -58,7 +57,6 @@ lineart_pipe = StyleContentStableDiffusionControlNetPipeline.from_pretrained(bas
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lineart_styleshot = StyleShot(device, lineart_pipe, lineart_ip_ckpt, lineart_style_aware_encoder_path, lineart_transformer_block_path)
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@spaces.GPU
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def process(style_image, content_image, prompt, num_samples, image_resolution, condition_scale, style_scale,ddim_steps, guidance_scale, seed, a_prompt, n_prompt, btn1, Contour_Threshold=200):
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weight_dtype = torch.float32
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@@ -143,7 +141,7 @@ with block:
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with gr.Column():
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style_image = gr.Image(sources=['upload'], type="numpy", label='Style Image')
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with gr.Column():
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with gr.
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with gr.Column():
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content_image = gr.Image(sources=['upload'], type="numpy", label='Content Image (optional)')
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btn1 = gr.Radio(
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@@ -177,16 +175,18 @@ with block:
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value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality')
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with gr.Row():
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with gr.Row():
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with gr.Column(scale = 1):
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contour_gallery = gr.Gallery(label='Contour Output', show_label=True, elem_id="gallery", columns=[1], rows=[1], height='auto')
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with gr.Column(scale = 4):
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image_gallery = gr.Gallery(label='Result for Contour', show_label=True, elem_id="gallery", columns=[4], rows=[1], height='auto')
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with gr.Row():
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with gr.Row():
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with gr.Column(scale = 1):
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line_gallery = gr.Gallery(label='Lineart Output', show_label=True, elem_id="gallery", columns=[1], rows=[1], height='auto')
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from types import MethodType
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import os
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import gradio as gr
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import torch
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from PIL import Image
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from ip_adapter import StyleShot, StyleContentStableDiffusionControlNetPipeline
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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contour_detector = SOFT_HEDdetector()
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lineart_detector = LineartDetector()
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lineart_styleshot = StyleShot(device, lineart_pipe, lineart_ip_ckpt, lineart_style_aware_encoder_path, lineart_transformer_block_path)
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def process(style_image, content_image, prompt, num_samples, image_resolution, condition_scale, style_scale,ddim_steps, guidance_scale, seed, a_prompt, n_prompt, btn1, Contour_Threshold=200):
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weight_dtype = torch.float32
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with gr.Column():
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style_image = gr.Image(sources=['upload'], type="numpy", label='Style Image')
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with gr.Column():
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with gr.Blocks():
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with gr.Column():
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content_image = gr.Image(sources=['upload'], type="numpy", label='Content Image (optional)')
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btn1 = gr.Radio(
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value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality')
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with gr.Row():
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gr.Markdown("### Results for Contour")
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with gr.Row():
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with gr.Blocks():
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with gr.Row():
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with gr.Column(scale = 1):
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contour_gallery = gr.Gallery(label='Contour Output', show_label=True, elem_id="gallery", columns=[1], rows=[1], height='auto')
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with gr.Column(scale = 4):
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image_gallery = gr.Gallery(label='Result for Contour', show_label=True, elem_id="gallery", columns=[4], rows=[1], height='auto')
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with gr.Row():
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gr.Markdown("### Results for Lineart")
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with gr.Row():
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with gr.Blocks():
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with gr.Row():
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with gr.Column(scale = 1):
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line_gallery = gr.Gallery(label='Lineart Output', show_label=True, elem_id="gallery", columns=[1], rows=[1], height='auto')
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requirements.txt
CHANGED
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@@ -8,5 +8,5 @@ accelerate==0.21.0
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opencv-python==4.8.1.78
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einops==0.7.0
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botocore==1.34.68
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gradio==
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basicsr
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opencv-python==4.8.1.78
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einops==0.7.0
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botocore==1.34.68
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gradio==4.37.2
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basicsr
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