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20231201.1
| Author | SHA1 | Date | |
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7b8655b715 |
@@ -159,6 +159,7 @@ class Image2ImagePipeline(StableDiffusionPipeline):
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cpu_scheduling,
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max_embeddings_multiples,
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resample_type,
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control_mode,
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):
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# prompts and negative prompts must be a list.
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if isinstance(prompts, str):
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@@ -34,6 +34,7 @@ from apps.stable_diffusion.src.utils import (
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from apps.stable_diffusion.src.utils.stencils import (
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CannyDetector,
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OpenposeDetector,
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ZoeDetector,
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)
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from apps.stable_diffusion.web.utils.common_label_calc import status_label
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import numpy as np
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@@ -99,6 +100,8 @@ def img2img_inf(
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if images[i] is None and stencil is not None:
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return None, "A stencil must have an Image input"
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if images[i] is not None:
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if isinstance(images[i], dict):
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images[i] = images[i]["layers"][0]
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images[i] = images[i].convert("RGB")
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if image_dict is None:
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@@ -363,71 +366,187 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
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# TODO: make this import image prompt info if it exists
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img2img_init_image = gr.Image(
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label="Input Image",
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source="upload",
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tool="sketch",
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type="pil",
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height=300,
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height=512,
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interactive=True,
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)
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with gr.Accordion(label="Multistencil Options", open=False):
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choices = ["None", "canny", "openpose", "scribble"]
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choices = [
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"None",
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"canny",
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"openpose",
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"scribble",
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"zoedepth",
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]
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def cnet_preview(
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checked, model, input_image, index, stencils, images
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model, input_image, index, stencils, images
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):
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if not checked:
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stencils[index] = None
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images[index] = None
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return (None, stencils, images)
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images[index] = input_image
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stencils[index] = model
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match model:
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case "canny":
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canny = CannyDetector()
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result = canny(np.array(input_image), 100, 200)
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result = canny(
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np.array(input_image["composite"]),
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100,
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200,
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)
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return (
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[Image.fromarray(result), result],
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Image.fromarray(result),
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stencils,
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images,
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)
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case "openpose":
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openpose = OpenposeDetector()
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result = openpose(np.array(input_image))
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result = openpose(
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np.array(input_image["composite"])
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)
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# TODO: This is just an empty canvas, need to draw the candidates (which are in result[1])
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return (
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[Image.fromarray(result[0]), result],
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Image.fromarray(result[0]),
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stencils,
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images,
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)
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case "zoedepth":
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zoedepth = ZoeDetector()
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result = zoedepth(
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np.array(input_image["composite"])
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)
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return (
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Image.fromarray(result[0]),
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stencils,
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images,
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)
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case "scribble":
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result = input_image["composite"].convert("L")
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return (result, stencils, images)
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case _:
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return (None, stencils, images)
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def create_canvas(width, height):
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return {
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"background": None,
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"layers": [
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Image.fromarray(
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np.zeros(
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shape=(height, width, 3),
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dtype=np.uint8,
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)
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+ 255
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)
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],
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"composite": None,
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}
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def update_cn_input(model, width, height):
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if model == "scribble":
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return [
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gr.ImageEditor(
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visible=True,
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image_mode="RGB",
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interactive=True,
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show_label=False,
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type="pil",
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value=create_canvas(width, height),
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crop_size=(width, height),
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),
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gr.Image(
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visible=True,
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show_label=False,
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interactive=False,
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),
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gr.Slider(visible=True),
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gr.Slider(visible=True),
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gr.Button(visible=True),
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]
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else:
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return [
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gr.ImageEditor(
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visible=True,
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image_mode="RGB",
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type="pil",
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interactive=True,
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value=None,
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),
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gr.Image(
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visible=True,
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show_label=False,
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interactive=True,
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),
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gr.Slider(visible=False),
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gr.Slider(visible=False),
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gr.Button(visible=False),
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]
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with gr.Row():
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cnet_1 = gr.Checkbox(show_label=False)
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cnet_1_model = gr.Dropdown(
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label="Controlnet 1",
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value="None",
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choices=choices,
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)
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cnet_1_image = gr.Image(
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source="upload",
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tool=None,
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with gr.Column():
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cnet_1 = gr.Button(
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value="Generate controlnet input"
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)
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cnet_1_model = gr.Dropdown(
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label="Controlnet 1",
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value="None",
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choices=choices,
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)
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canvas_width = gr.Slider(
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label="Canvas Width",
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minimum=256,
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maximum=1024,
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value=512,
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step=1,
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visible=False,
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)
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canvas_height = gr.Slider(
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label="Canvas Height",
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minimum=256,
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maximum=1024,
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value=512,
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step=1,
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visible=False,
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)
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make_canvas = gr.Button(
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value="Make Canvas!",
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visible=False,
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)
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cnet_1_image = gr.ImageEditor(
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visible=False,
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image_mode="RGB",
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interactive=True,
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show_label=False,
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type="pil",
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)
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cnet_1_output = gr.Gallery(
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show_label=False,
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object_fit="scale-down",
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rows=1,
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columns=1,
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cnet_1_output = gr.Image(
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visible=True, show_label=False
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)
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cnet_1.change(
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cnet_1_model.input(
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update_cn_input,
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[cnet_1_model, canvas_width, canvas_height],
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[
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cnet_1_image,
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cnet_1_output,
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canvas_width,
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canvas_height,
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make_canvas,
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],
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)
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make_canvas.click(
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update_cn_input,
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[cnet_1_model, canvas_width, canvas_height],
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[
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cnet_1_image,
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cnet_1_output,
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canvas_width,
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canvas_height,
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make_canvas,
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],
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)
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cnet_1.click(
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fn=(
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lambda a, b, c, s, i: cnet_preview(
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a, b, c, 0, s, i
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)
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lambda a, b, s, i: cnet_preview(a, b, 0, s, i)
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),
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inputs=[
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cnet_1,
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cnet_1_model,
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cnet_1_image,
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stencils,
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@@ -436,31 +555,72 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
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outputs=[cnet_1_output, stencils, images],
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)
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with gr.Row():
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cnet_2 = gr.Checkbox(show_label=False)
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cnet_2_model = gr.Dropdown(
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label="Controlnet 2",
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value="None",
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choices=choices,
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)
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cnet_2_image = gr.Image(
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source="upload",
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tool=None,
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with gr.Column():
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cnet_2 = gr.Button(
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value="Generate controlnet input"
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)
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cnet_2_model = gr.Dropdown(
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label="Controlnet 2",
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value="None",
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choices=choices,
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)
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canvas_width = gr.Slider(
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label="Canvas Width",
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minimum=256,
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maximum=1024,
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value=512,
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step=1,
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visible=False,
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)
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canvas_height = gr.Slider(
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label="Canvas Height",
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minimum=256,
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maximum=1024,
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value=512,
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step=1,
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visible=False,
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)
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make_canvas = gr.Button(
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value="Make Canvas!",
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visible=False,
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)
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cnet_2_image = gr.ImageEditor(
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visible=False,
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image_mode="RGB",
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interactive=True,
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show_label=False,
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type="pil",
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)
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cnet_2_output = gr.Gallery(
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show_label=False,
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object_fit="scale-down",
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rows=1,
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columns=1,
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cnet_2_output = gr.Image(
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visible=True, show_label=False
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)
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cnet_2.change(
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cnet_2_model.select(
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update_cn_input,
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[cnet_2_model, canvas_width, canvas_height],
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[
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cnet_2_image,
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cnet_2_output,
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canvas_width,
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canvas_height,
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make_canvas,
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],
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)
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make_canvas.click(
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update_cn_input,
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[cnet_2_model, canvas_width, canvas_height],
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[
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cnet_2_image,
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cnet_2_output,
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canvas_width,
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canvas_height,
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make_canvas,
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],
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)
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cnet_2.click(
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fn=(
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lambda a, b, c, s, i: cnet_preview(
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a, b, c, 1, s, i
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)
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lambda a, b, s, i: cnet_preview(a, b, 1, s, i)
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),
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inputs=[
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cnet_2,
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cnet_2_model,
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cnet_2_image,
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stencils,
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@@ -288,10 +288,9 @@ with gr.Blocks(title="Inpainting") as inpaint_web:
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elem_id="negative_prompt_box",
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)
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inpaint_init_image = gr.Image(
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inpaint_init_image = gr.ImageEditor(
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label="Masked Image",
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source="upload",
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tool="sketch",
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sources="upload",
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type="pil",
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height=350,
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)
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@@ -104,7 +104,6 @@ with gr.Blocks() as model_web:
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civit_models = gr.Gallery(
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label="Civitai Model Gallery",
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value=None,
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interactive=True,
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visible=False,
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)
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@@ -291,7 +291,7 @@ with gr.Blocks(title="Outpainting") as outpaint_web:
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elem_id="negative_prompt_box",
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)
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outpaint_init_image = gr.Image(
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outpaint_init_image = gr.ImageEditor(
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label="Input Image",
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type="pil",
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height=300,
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@@ -95,7 +95,7 @@ with gr.Blocks() as outputgallery_web:
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)
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with gr.Column(scale=4):
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with gr.Box():
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with gr.Group():
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with gr.Row():
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with gr.Column(
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scale=15,
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@@ -254,7 +254,7 @@ with gr.Blocks() as outputgallery_web:
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)
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return [
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gr.Dropdown.update(
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gr.Dropdown(
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choices=refreshed_subdirs,
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value=new_subdir,
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),
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@@ -431,8 +431,8 @@ with gr.Blocks(title="Chatbot") as stablelm_chat:
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config_file = gr.File(
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label="Upload sharding configuration", visible=False
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)
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json_view_button = gr.Button(label="View as JSON", visible=False)
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json_view = gr.JSON(interactive=True, visible=False)
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json_view_button = gr.Button(value="View as JSON", visible=False)
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json_view = gr.JSON(visible=False)
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json_view_button.click(
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fn=view_json_file, inputs=[config_file], outputs=[json_view]
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)
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@@ -356,7 +356,6 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
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label="Import PNG info",
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elem_id="txt2img_prompt_image",
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type="pil",
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tool="None",
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visible=True,
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)
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@@ -311,7 +311,7 @@ with gr.Blocks(title="Upscaler") as upscaler_web:
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elem_id="negative_prompt_box",
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)
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upscaler_init_image = gr.Image(
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upscaler_init_image = gr.ImageEditor(
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label="Input Image",
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type="pil",
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height=300,
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@@ -26,7 +26,7 @@ diffusers
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accelerate
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scipy
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ftfy
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gradio==3.44.3
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gradio==4.7.1
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altair
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omegaconf
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# 0.3.2 doesn't have binaries for arm64
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