Spaces:
Running
on
Zero
Running
on
Zero
Himanshu-AT
commited on
Commit
·
e08bb94
1
Parent(s):
fceb263
add sam
Browse files- app.py +18 -6
- lora_models.json +2 -1
- requirements.txt +2 -1
app.py
CHANGED
@@ -1,24 +1,24 @@
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import gradio as gr
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import numpy as np
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import os
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import spaces
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import random
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import json
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# from image_gen_aux import DepthPreprocessor
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from PIL import Image
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import torch
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from torchvision import transforms
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from diffusers import FluxFillPipeline, AutoencoderKL
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from PIL import Image
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 2048
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pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16).to("cuda")
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# pipe.load_lora_weights("Himanshu806/testLora")
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# pipe.enable_lora()
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with open("lora_models.json", "r") as f:
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lora_models = json.load(f)
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@@ -119,6 +119,11 @@ def download_image(image):
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def set_image_as_inpaint(image):
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return image
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examples = [
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"photography of a young woman, accent lighting, (front view:1.4), "
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# "a tiny astronaut hatching from an egg on the moon",
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@@ -237,6 +242,12 @@ with gr.Blocks(css=css) as demo:
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outputs=[edit_image]
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)
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# demo.launch()
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PASSWORD = os.getenv("GRADIO_PASSWORD")
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USERNAME = os.getenv("GRADIO_USERNAME")
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@@ -249,7 +260,8 @@ def authenticate(username, password):
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return False
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# Launch the app with authentication
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demo.launch(auth=authenticate)
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# import gradio as gr
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import spaces
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import gradio as gr
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import numpy as np
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import os
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import random
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import json
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from PIL import Image
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import torch
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from torchvision import transforms
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from diffusers import FluxFillPipeline, AutoencoderKL
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from PIL import Image
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from samgeo.text_sam import LangSAM
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 2048
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sam = LangSAM(model_type="sam2-hiera-large")
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pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16).to("cuda")
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with open("lora_models.json", "r") as f:
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lora_models = json.load(f)
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def set_image_as_inpaint(image):
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return image
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def generate_mask(image, click_x, click_y):
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text_prompt = "face"
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mask = sam.predict(image, text_prompt, box_threshold=0.24, text_threshold=0.24)
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return mask
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examples = [
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"photography of a young woman, accent lighting, (front view:1.4), "
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# "a tiny astronaut hatching from an egg on the moon",
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outputs=[edit_image]
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)
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edit_image.select(
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fn=generate_mask,
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inputs=[edit_image, gr.Number(), gr.Number()],
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outputs=[edit_image]
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)
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# demo.launch()
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PASSWORD = os.getenv("GRADIO_PASSWORD")
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USERNAME = os.getenv("GRADIO_USERNAME")
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return False
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# Launch the app with authentication
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demo.launch(debug=True, auth=authenticate)
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# demo.launch()
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# import gradio as gr
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lora_models.json
CHANGED
@@ -1,5 +1,6 @@
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{
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"RahulFineTuned (qwertyui)": "Himanshu806/testLora",
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"KodaRealistic (fmlft style)": "alvdansen/flux-koda",
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-
"
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}
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{
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"RahulFineTuned (qwertyui)": "Himanshu806/testLora",
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"femaleIndian (indmodelf)": "Himanshu806/ind-f-model",
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"KodaRealistic (fmlft style)": "alvdansen/flux-koda",
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"superRealism (Super Realism)": "strangerzonehf/Flux-Super-Realism-LoRA"
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}
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requirements.txt
CHANGED
@@ -8,4 +8,5 @@ peft
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xformers
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torchvision
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torch
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opencv-python
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xformers
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torchvision
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torch
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opencv-python
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segment-geospatial
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