mirror of
https://github.com/nod-ai/AMD-SHARK-Studio.git
synced 2026-04-03 03:00:17 -04:00
SD/UI: Use a single model selection box on UI tabs (#1906)
* Allow entry of a huggingface model id or civitai download url to be done in the main model selection dropdown on SD tabs * Remove separate textbox for entering huggingface model id or civitai download url on SD Tabs * Remove 'None' option from the model selection dropdown (no longer needed) on SD tabs * Update png metadata drop zone on txt2img tab to work with a single argument for model selection * Update UI generate functions on SD tabs to work with single argument model selection * Update API code for changes to the UI generate functions * Move info about the custom model path to the logging textarea on SD tabs
This commit is contained in:
Stefan Kapusniak
@@ -110,7 +110,6 @@ if __name__ == "__main__":
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from apps.stable_diffusion.web.ui import (
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txt2img_web,
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txt2img_custom_model,
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txt2img_hf_model_id,
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txt2img_gallery,
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txt2img_png_info_img,
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txt2img_status,
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@@ -122,7 +121,6 @@ if __name__ == "__main__":
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# h2ogpt_web,
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img2img_web,
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img2img_custom_model,
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img2img_hf_model_id,
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img2img_gallery,
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img2img_init_image,
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img2img_status,
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@@ -131,7 +129,6 @@ if __name__ == "__main__":
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img2img_sendto_upscaler,
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inpaint_web,
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inpaint_custom_model,
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inpaint_hf_model_id,
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inpaint_gallery,
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inpaint_init_image,
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inpaint_status,
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@@ -140,7 +137,6 @@ if __name__ == "__main__":
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inpaint_sendto_upscaler,
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outpaint_web,
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outpaint_custom_model,
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outpaint_hf_model_id,
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outpaint_gallery,
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outpaint_init_image,
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outpaint_status,
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@@ -149,7 +145,6 @@ if __name__ == "__main__":
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outpaint_sendto_upscaler,
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upscaler_web,
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upscaler_custom_model,
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upscaler_hf_model_id,
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upscaler_gallery,
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upscaler_init_image,
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upscaler_status,
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@@ -399,31 +394,31 @@ if __name__ == "__main__":
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modelmanager_sendto_txt2img,
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0,
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[hf_models],
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[txt2img_custom_model, txt2img_hf_model_id, tabs],
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[txt2img_custom_model, tabs],
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)
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register_modelmanager_button(
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modelmanager_sendto_img2img,
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1,
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[hf_models],
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[img2img_custom_model, img2img_hf_model_id, tabs],
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[img2img_custom_model, tabs],
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)
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register_modelmanager_button(
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modelmanager_sendto_inpaint,
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2,
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[hf_models],
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[inpaint_custom_model, inpaint_hf_model_id, tabs],
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[inpaint_custom_model, tabs],
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)
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register_modelmanager_button(
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modelmanager_sendto_outpaint,
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3,
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[hf_models],
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[outpaint_custom_model, outpaint_hf_model_id, tabs],
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[outpaint_custom_model, tabs],
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)
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register_modelmanager_button(
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modelmanager_sendto_upscaler,
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4,
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[hf_models],
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[upscaler_custom_model, upscaler_hf_model_id, tabs],
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[upscaler_custom_model, tabs],
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)
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sd_web.queue()
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@@ -3,7 +3,6 @@ from apps.stable_diffusion.web.ui.txt2img_ui import (
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txt2img_api,
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txt2img_web,
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txt2img_custom_model,
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txt2img_hf_model_id,
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txt2img_gallery,
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txt2img_png_info_img,
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txt2img_status,
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@@ -17,7 +16,6 @@ from apps.stable_diffusion.web.ui.img2img_ui import (
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img2img_api,
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img2img_web,
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img2img_custom_model,
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img2img_hf_model_id,
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img2img_gallery,
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img2img_init_image,
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img2img_status,
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@@ -30,7 +28,6 @@ from apps.stable_diffusion.web.ui.inpaint_ui import (
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inpaint_api,
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inpaint_web,
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inpaint_custom_model,
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inpaint_hf_model_id,
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inpaint_gallery,
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inpaint_init_image,
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inpaint_status,
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@@ -43,7 +40,6 @@ from apps.stable_diffusion.web.ui.outpaint_ui import (
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outpaint_api,
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outpaint_web,
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outpaint_custom_model,
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outpaint_hf_model_id,
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outpaint_gallery,
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outpaint_init_image,
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outpaint_status,
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@@ -56,7 +52,6 @@ from apps.stable_diffusion.web.ui.upscaler_ui import (
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upscaler_api,
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upscaler_web,
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upscaler_custom_model,
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upscaler_hf_model_id,
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upscaler_gallery,
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upscaler_init_image,
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upscaler_status,
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@@ -55,8 +55,7 @@ def img2img_inf(
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batch_count: int,
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batch_size: int,
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scheduler: str,
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custom_model: str,
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hf_model_id: str,
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model_id: str,
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custom_vae: str,
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precision: str,
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device: str,
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@@ -103,21 +102,17 @@ def img2img_inf(
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args.ckpt_loc = ""
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args.hf_model_id = ""
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args.custom_vae = ""
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if custom_model == "None":
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if not hf_model_id:
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return (
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None,
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"Please provide either custom model or huggingface model ID, "
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"both must not be empty.",
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)
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if "civitai" in hf_model_id:
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args.ckpt_loc = hf_model_id
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else:
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args.hf_model_id = hf_model_id
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elif ".ckpt" in custom_model or ".safetensors" in custom_model:
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args.ckpt_loc = get_custom_model_pathfile(custom_model)
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# .safetensor or .chkpt on the custom model path
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if model_id in get_custom_model_files():
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args.ckpt_loc = get_custom_model_pathfile(model_id)
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# civitai download
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elif "civitai" in model_id:
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args.ckpt_loc = model_id
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# either predefined or huggingface
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else:
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args.hf_model_id = custom_model
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args.hf_model_id = model_id
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if custom_vae != "None":
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args.custom_vae = get_custom_model_pathfile(custom_vae, model="vae")
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@@ -334,8 +329,7 @@ def img2img_api(
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batch_count=1,
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batch_size=1,
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scheduler="EulerDiscrete",
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custom_model="None",
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hf_model_id=InputData["hf_model_id"]
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model_id=InputData["hf_model_id"]
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if "hf_model_id" in InputData.keys()
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else "stabilityai/stable-diffusion-2-1-base",
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custom_vae="None",
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@@ -382,32 +376,19 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
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with gr.Column(scale=1, min_width=600):
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with gr.Row():
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# janky fix for overflowing text
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i2i_model_info = (str(get_custom_model_path())).replace(
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"\\", "\n\\"
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i2i_model_info = (
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f"Custom Model Path: {str(get_custom_model_path())}"
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)
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i2i_model_info = f"Custom Model Path: {i2i_model_info}"
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img2img_custom_model = gr.Dropdown(
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label=f"Models",
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info=i2i_model_info,
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info="Select, or enter HuggingFace Model ID or Civitai model download URL",
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elem_id="custom_model",
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value=os.path.basename(args.ckpt_loc)
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if args.ckpt_loc
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else "stabilityai/stable-diffusion-2-1-base",
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choices=["None"]
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+ get_custom_model_files()
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+ predefined_models,
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choices=get_custom_model_files() + predefined_models,
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allow_custom_value=True,
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)
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img2img_hf_model_id = gr.Textbox(
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elem_id="hf_model_id",
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placeholder="Select 'None' in the Models dropdown "
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"on the left and enter model ID here "
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"e.g: SG161222/Realistic_Vision_V1.3, "
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"https://civitai.com/api/download/models/15236",
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value="",
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label="HuggingFace Model ID or Civitai model "
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"download URL",
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lines=3,
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scale=2,
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)
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# janky fix for overflowing text
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i2i_vae_info = (str(get_custom_model_path("vae"))).replace(
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@@ -423,6 +404,7 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
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else "None",
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choices=["None"] + get_custom_model_files("vae"),
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allow_custom_value=True,
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scale=1,
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)
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with gr.Group(elem_id="prompt_box_outer"):
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@@ -677,9 +659,10 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
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object_fit="contain",
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)
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std_output = gr.Textbox(
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value=f"Images will be saved at "
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value=f"{i2i_model_info}\n"
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f"Images will be saved at "
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f"{get_generated_imgs_path()}",
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lines=1,
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lines=2,
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elem_id="std_output",
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show_label=False,
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)
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@@ -709,7 +692,6 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
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batch_size,
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scheduler,
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img2img_custom_model,
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img2img_hf_model_id,
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custom_vae,
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precision,
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device,
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@@ -53,8 +53,7 @@ def inpaint_inf(
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batch_count: int,
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batch_size: int,
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scheduler: str,
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custom_model: str,
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hf_model_id: str,
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model_id: str,
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custom_vae: str,
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precision: str,
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device: str,
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@@ -89,21 +88,17 @@ def inpaint_inf(
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args.ckpt_loc = ""
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args.hf_model_id = ""
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args.custom_vae = ""
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if custom_model == "None":
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if not hf_model_id:
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return (
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None,
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"Please provide either custom model or huggingface model ID, "
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"both must not be empty.",
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)
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if "civitai" in hf_model_id:
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args.ckpt_loc = hf_model_id
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else:
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args.hf_model_id = hf_model_id
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elif ".ckpt" in custom_model or ".safetensors" in custom_model:
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args.ckpt_loc = get_custom_model_pathfile(custom_model)
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# .safetensor or .chkpt on the custom model path
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if model_id in get_custom_model_files(custom_checkpoint_type="inpainting"):
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args.ckpt_loc = get_custom_model_pathfile(model_id)
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# civitai download
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elif "civitai" in model_id:
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args.ckpt_loc = model_id
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# either predefined or huggingface
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else:
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args.hf_model_id = custom_model
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args.hf_model_id = model_id
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if custom_vae != "None":
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args.custom_vae = get_custom_model_pathfile(custom_vae, model="vae")
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@@ -282,8 +277,7 @@ def inpaint_api(
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batch_count=1,
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batch_size=1,
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scheduler="EulerDiscrete",
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custom_model="None",
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hf_model_id=InputData["hf_model_id"]
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model_id=InputData["hf_model_id"]
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if "hf_model_id" in InputData.keys()
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else "stabilityai/stable-diffusion-2-inpainting",
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custom_vae="None",
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@@ -327,35 +321,21 @@ with gr.Blocks(title="Inpainting") as inpaint_web:
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with gr.Row():
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# janky fix for overflowing text
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inpaint_model_info = (
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str(get_custom_model_path())
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).replace("\\", "\n\\")
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inpaint_model_info = (
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f"Custom Model Path: {inpaint_model_info}"
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f"Custom Model Path: {str(get_custom_model_path())}"
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)
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inpaint_custom_model = gr.Dropdown(
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label=f"Models",
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info=inpaint_model_info,
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info="Select, or enter HuggingFace Model ID or Civitai model download URL",
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elem_id="custom_model",
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value=os.path.basename(args.ckpt_loc)
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if args.ckpt_loc
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else "stabilityai/stable-diffusion-2-inpainting",
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choices=["None"]
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+ get_custom_model_files(
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choices=get_custom_model_files(
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custom_checkpoint_type="inpainting"
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)
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+ predefined_paint_models,
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allow_custom_value=True,
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)
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inpaint_hf_model_id = gr.Textbox(
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elem_id="hf_model_id",
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placeholder="Select 'None' in the Models dropdown "
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"on the left and enter model ID here "
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"e.g: ghunkins/stable-diffusion-liberty-inpainting, "
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"https://civitai.com/api/download/models/3433",
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value="",
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label="HuggingFace Model ID or Civitai model "
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"download URL",
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lines=3,
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scale=2,
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)
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# janky fix for overflowing text
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inpaint_vae_info = (
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@@ -371,6 +351,7 @@ with gr.Blocks(title="Inpainting") as inpaint_web:
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else "None",
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choices=["None"] + get_custom_model_files("vae"),
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allow_custom_value=True,
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scale=1,
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)
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with gr.Group(elem_id="prompt_box_outer"):
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@@ -554,9 +535,10 @@ with gr.Blocks(title="Inpainting") as inpaint_web:
|
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object_fit="contain",
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)
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std_output = gr.Textbox(
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value=f"Images will be saved at "
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value=f"{inpaint_model_info}\n"
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"Images will be saved at "
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f"{get_generated_imgs_path()}",
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lines=1,
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lines=2,
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elem_id="std_output",
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show_label=False,
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)
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@@ -588,7 +570,6 @@ with gr.Blocks(title="Inpainting") as inpaint_web:
|
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batch_size,
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scheduler,
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inpaint_custom_model,
|
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inpaint_hf_model_id,
|
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custom_vae,
|
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precision,
|
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device,
|
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|
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@@ -53,8 +53,7 @@ def outpaint_inf(
|
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batch_count: int,
|
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batch_size: int,
|
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scheduler: str,
|
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custom_model: str,
|
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hf_model_id: str,
|
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model_id: str,
|
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custom_vae: str,
|
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precision: str,
|
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device: str,
|
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@@ -88,21 +87,17 @@ def outpaint_inf(
|
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args.ckpt_loc = ""
|
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args.hf_model_id = ""
|
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args.custom_vae = ""
|
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if custom_model == "None":
|
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if not hf_model_id:
|
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return (
|
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None,
|
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"Please provide either custom model or huggingface model ID, "
|
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"both must not be empty.",
|
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)
|
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if "civitai" in hf_model_id:
|
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args.ckpt_loc = hf_model_id
|
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else:
|
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args.hf_model_id = hf_model_id
|
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elif ".ckpt" in custom_model or ".safetensors" in custom_model:
|
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args.ckpt_loc = get_custom_model_pathfile(custom_model)
|
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|
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# .safetensor or .chkpt on the custom model path
|
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if model_id in get_custom_model_files(custom_checkpoint_type="inpainting"):
|
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args.ckpt_loc = get_custom_model_pathfile(model_id)
|
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# civitai download
|
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elif "civitai" in model_id:
|
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args.ckpt_loc = model_id
|
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# either predefined or huggingface
|
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else:
|
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args.hf_model_id = custom_model
|
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args.hf_model_id = model_id
|
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|
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if custom_vae != "None":
|
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args.custom_vae = get_custom_model_pathfile(custom_vae, model="vae")
|
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|
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@@ -289,8 +284,7 @@ def outpaint_api(
|
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batch_count=1,
|
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batch_size=1,
|
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scheduler="EulerDiscrete",
|
||||
custom_model="None",
|
||||
hf_model_id=InputData["hf_model_id"]
|
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model_id=InputData["hf_model_id"]
|
||||
if "hf_model_id" in InputData.keys()
|
||||
else "stabilityai/stable-diffusion-2-inpainting",
|
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custom_vae="None",
|
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@@ -332,37 +326,22 @@ with gr.Blocks(title="Outpainting") as outpaint_web:
|
||||
with gr.Row():
|
||||
with gr.Column(scale=1, min_width=600):
|
||||
with gr.Row():
|
||||
# janky fix for overflowing text
|
||||
outpaint_model_info = (
|
||||
str(get_custom_model_path())
|
||||
).replace("\\", "\n\\")
|
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outpaint_model_info = (
|
||||
f"Custom Model Path: {outpaint_model_info}"
|
||||
f"Custom Model Path: {str(get_custom_model_path())}"
|
||||
)
|
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outpaint_custom_model = gr.Dropdown(
|
||||
label=f"Models",
|
||||
info=outpaint_model_info,
|
||||
info="Select, or enter HuggingFace Model ID or Civitai model download URL",
|
||||
elem_id="custom_model",
|
||||
value=os.path.basename(args.ckpt_loc)
|
||||
if args.ckpt_loc
|
||||
else "stabilityai/stable-diffusion-2-inpainting",
|
||||
choices=["None"]
|
||||
+ get_custom_model_files(
|
||||
choices=get_custom_model_files(
|
||||
custom_checkpoint_type="inpainting"
|
||||
)
|
||||
+ predefined_paint_models,
|
||||
allow_custom_value=True,
|
||||
)
|
||||
outpaint_hf_model_id = gr.Textbox(
|
||||
elem_id="hf_model_id",
|
||||
placeholder="Select 'None' in the Models dropdown "
|
||||
"on the left and enter model ID here "
|
||||
"e.g: ghunkins/stable-diffusion-liberty-inpainting, "
|
||||
"https://civitai.com/api/download/models/3433",
|
||||
value="",
|
||||
label="HuggingFace Model ID or Civitai model "
|
||||
"download URL",
|
||||
lines=3,
|
||||
scale=2,
|
||||
)
|
||||
# janky fix for overflowing text
|
||||
outpaint_vae_info = (
|
||||
@@ -378,8 +357,8 @@ with gr.Blocks(title="Outpainting") as outpaint_web:
|
||||
else "None",
|
||||
choices=["None"] + get_custom_model_files("vae"),
|
||||
allow_custom_value=True,
|
||||
scale=1,
|
||||
)
|
||||
|
||||
with gr.Group(elem_id="prompt_box_outer"):
|
||||
prompt = gr.Textbox(
|
||||
label="Prompt",
|
||||
@@ -582,9 +561,10 @@ with gr.Blocks(title="Outpainting") as outpaint_web:
|
||||
object_fit="contain",
|
||||
)
|
||||
std_output = gr.Textbox(
|
||||
value=f"Images will be saved at "
|
||||
value=f"{outpaint_model_info}\n"
|
||||
f"Images will be saved at "
|
||||
f"{get_generated_imgs_path()}",
|
||||
lines=1,
|
||||
lines=2,
|
||||
elem_id="std_output",
|
||||
show_label=False,
|
||||
)
|
||||
@@ -616,7 +596,6 @@ with gr.Blocks(title="Outpainting") as outpaint_web:
|
||||
batch_size,
|
||||
scheduler,
|
||||
outpaint_custom_model,
|
||||
outpaint_hf_model_id,
|
||||
custom_vae,
|
||||
precision,
|
||||
device,
|
||||
|
||||
@@ -52,8 +52,7 @@ def txt2img_inf(
|
||||
batch_count: int,
|
||||
batch_size: int,
|
||||
scheduler: str,
|
||||
custom_model: str,
|
||||
hf_model_id: str,
|
||||
model_id: str,
|
||||
custom_vae: str,
|
||||
precision: str,
|
||||
device: str,
|
||||
@@ -91,21 +90,17 @@ def txt2img_inf(
|
||||
args.ckpt_loc = ""
|
||||
args.hf_model_id = ""
|
||||
args.custom_vae = ""
|
||||
if custom_model == "None":
|
||||
if not hf_model_id:
|
||||
return (
|
||||
None,
|
||||
"Please provide either custom model or huggingface model ID, "
|
||||
"both must not be empty",
|
||||
)
|
||||
if "civitai" in hf_model_id:
|
||||
args.ckpt_loc = hf_model_id
|
||||
else:
|
||||
args.hf_model_id = hf_model_id
|
||||
elif ".ckpt" in custom_model or ".safetensors" in custom_model:
|
||||
args.ckpt_loc = get_custom_model_pathfile(custom_model)
|
||||
|
||||
# .safetensor or .chkpt on the custom model path
|
||||
if model_id in get_custom_model_files():
|
||||
args.ckpt_loc = get_custom_model_pathfile(model_id)
|
||||
# civitai download
|
||||
elif "civitai" in model_id:
|
||||
args.ckpt_loc = model_id
|
||||
# either predefined or huggingface
|
||||
else:
|
||||
args.hf_model_id = custom_model
|
||||
args.hf_model_id = model_id
|
||||
|
||||
if custom_vae != "None":
|
||||
args.custom_vae = get_custom_model_pathfile(custom_vae, model="vae")
|
||||
|
||||
@@ -339,8 +334,7 @@ def txt2img_api(
|
||||
batch_count=1,
|
||||
batch_size=1,
|
||||
scheduler="EulerDiscrete",
|
||||
custom_model="None",
|
||||
hf_model_id=InputData["hf_model_id"]
|
||||
model_id=InputData["hf_model_id"]
|
||||
if "hf_model_id" in InputData.keys()
|
||||
else "stabilityai/stable-diffusion-2-1-base",
|
||||
custom_vae="None",
|
||||
@@ -389,33 +383,18 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
|
||||
with gr.Row():
|
||||
with gr.Column(scale=10):
|
||||
with gr.Row():
|
||||
# janky fix for overflowing text
|
||||
t2i_model_info = (
|
||||
str(get_custom_model_path())
|
||||
).replace("\\", "\n\\")
|
||||
t2i_model_info = (
|
||||
f"Custom Model Path: {t2i_model_info}"
|
||||
)
|
||||
t2i_model_info = f"Custom Model Path: {str(get_custom_model_path())}"
|
||||
txt2img_custom_model = gr.Dropdown(
|
||||
label=f"Models",
|
||||
info=t2i_model_info,
|
||||
info="Select, or enter HuggingFace Model ID or Civitai model download URL",
|
||||
elem_id="custom_model",
|
||||
value=os.path.basename(args.ckpt_loc)
|
||||
if args.ckpt_loc
|
||||
else "stabilityai/stable-diffusion-2-1-base",
|
||||
choices=["None"]
|
||||
+ get_custom_model_files()
|
||||
choices=get_custom_model_files()
|
||||
+ predefined_models,
|
||||
allow_custom_value=True,
|
||||
)
|
||||
txt2img_hf_model_id = gr.Textbox(
|
||||
elem_id="hf_model_id",
|
||||
placeholder="Select 'None' in the dropdown "
|
||||
"on the left and enter model ID here.",
|
||||
value="",
|
||||
label="HuggingFace Model ID or Civitai model "
|
||||
"download URL.",
|
||||
lines=3,
|
||||
scale=2,
|
||||
)
|
||||
# janky fix for overflowing text
|
||||
t2i_vae_info = (
|
||||
@@ -432,6 +411,7 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
|
||||
choices=["None"]
|
||||
+ get_custom_model_files("vae"),
|
||||
allow_custom_value=True,
|
||||
scale=1,
|
||||
)
|
||||
with gr.Column(scale=1, min_width=170):
|
||||
txt2img_png_info_img = gr.Image(
|
||||
@@ -649,7 +629,8 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
|
||||
object_fit="contain",
|
||||
)
|
||||
std_output = gr.Textbox(
|
||||
value=f"Images will be saved at "
|
||||
value=f"{t2i_model_info}\n"
|
||||
f"Images will be saved at "
|
||||
f"{get_generated_imgs_path()}",
|
||||
lines=1,
|
||||
elem_id="std_output",
|
||||
@@ -692,7 +673,6 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
|
||||
batch_size,
|
||||
scheduler,
|
||||
txt2img_custom_model,
|
||||
txt2img_hf_model_id,
|
||||
custom_vae,
|
||||
precision,
|
||||
device,
|
||||
@@ -742,7 +722,6 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
|
||||
width,
|
||||
height,
|
||||
txt2img_custom_model,
|
||||
txt2img_hf_model_id,
|
||||
lora_weights,
|
||||
lora_hf_id,
|
||||
custom_vae,
|
||||
@@ -758,7 +737,6 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
|
||||
width,
|
||||
height,
|
||||
txt2img_custom_model,
|
||||
txt2img_hf_model_id,
|
||||
lora_weights,
|
||||
lora_hf_id,
|
||||
custom_vae,
|
||||
|
||||
@@ -46,8 +46,7 @@ def upscaler_inf(
|
||||
batch_count: int,
|
||||
batch_size: int,
|
||||
scheduler: str,
|
||||
custom_model: str,
|
||||
hf_model_id: str,
|
||||
model_id: str,
|
||||
custom_vae: str,
|
||||
precision: str,
|
||||
device: str,
|
||||
@@ -85,21 +84,17 @@ def upscaler_inf(
|
||||
args.ckpt_loc = ""
|
||||
args.hf_model_id = ""
|
||||
args.custom_vae = ""
|
||||
if custom_model == "None":
|
||||
if not hf_model_id:
|
||||
return (
|
||||
None,
|
||||
"Please provide either custom model or huggingface model ID, "
|
||||
"both must not be empty.",
|
||||
)
|
||||
if "civitai" in hf_model_id:
|
||||
args.ckpt_loc = hf_model_id
|
||||
else:
|
||||
args.hf_model_id = hf_model_id
|
||||
elif ".ckpt" in custom_model or ".safetensors" in custom_model:
|
||||
args.ckpt_loc = get_custom_model_pathfile(custom_model)
|
||||
|
||||
# .safetensor or .chkpt on the custom model path
|
||||
if model_id in get_custom_model_files(custom_checkpoint_type="upscaler"):
|
||||
args.ckpt_loc = get_custom_model_pathfile(model_id)
|
||||
# civitai download
|
||||
elif "civitai" in model_id:
|
||||
args.ckpt_loc = model_id
|
||||
# either predefined or huggingface
|
||||
else:
|
||||
args.hf_model_id = custom_model
|
||||
args.hf_model_id = model_id
|
||||
|
||||
if custom_vae != "None":
|
||||
args.custom_vae = get_custom_model_pathfile(custom_vae, model="vae")
|
||||
|
||||
@@ -304,8 +299,7 @@ def upscaler_api(
|
||||
batch_count=1,
|
||||
batch_size=1,
|
||||
scheduler="EulerDiscrete",
|
||||
custom_model="None",
|
||||
hf_model_id=InputData["hf_model_id"]
|
||||
model_id=InputData["hf_model_id"]
|
||||
if "hf_model_id" in InputData.keys()
|
||||
else "stabilityai/stable-diffusion-2-1-base",
|
||||
custom_vae="None",
|
||||
@@ -346,37 +340,22 @@ with gr.Blocks(title="Upscaler") as upscaler_web:
|
||||
with gr.Row():
|
||||
with gr.Column(scale=1, min_width=600):
|
||||
with gr.Row():
|
||||
# janky fix for overflowing text
|
||||
upscaler_model_info = (
|
||||
str(get_custom_model_path())
|
||||
).replace("\\", "\n\\")
|
||||
upscaler_model_info = (
|
||||
f"Custom Model Path: {upscaler_model_info}"
|
||||
f"Custom Model Path: {str(get_custom_model_path())}"
|
||||
)
|
||||
upscaler_custom_model = gr.Dropdown(
|
||||
label=f"Models",
|
||||
info=upscaler_model_info,
|
||||
info="Select, or enter HuggingFace Model ID or Civitai model download URL",
|
||||
elem_id="custom_model",
|
||||
value=os.path.basename(args.ckpt_loc)
|
||||
if args.ckpt_loc
|
||||
else "stabilityai/stable-diffusion-x4-upscaler",
|
||||
choices=["None"]
|
||||
+ get_custom_model_files(
|
||||
choices=get_custom_model_files(
|
||||
custom_checkpoint_type="upscaler"
|
||||
)
|
||||
+ predefined_upscaler_models,
|
||||
allow_custom_value=True,
|
||||
)
|
||||
upscaler_hf_model_id = gr.Textbox(
|
||||
elem_id="hf_model_id",
|
||||
placeholder="Select 'None' in the Models dropdown "
|
||||
"on the left and enter model ID here "
|
||||
"e.g: SG161222/Realistic_Vision_V1.3, "
|
||||
"https://civitai.com/api/download/models/15236",
|
||||
value="",
|
||||
label="HuggingFace Model ID or Civitai model "
|
||||
"download URL",
|
||||
lines=3,
|
||||
scale=2,
|
||||
)
|
||||
# janky fix for overflowing text
|
||||
upscaler_vae_info = (
|
||||
@@ -392,6 +371,7 @@ with gr.Blocks(title="Upscaler") as upscaler_web:
|
||||
else "None",
|
||||
choices=["None"] + get_custom_model_files("vae"),
|
||||
allow_custom_value=True,
|
||||
scale=1,
|
||||
)
|
||||
|
||||
with gr.Group(elem_id="prompt_box_outer"):
|
||||
@@ -574,9 +554,10 @@ with gr.Blocks(title="Upscaler") as upscaler_web:
|
||||
object_fit="contain",
|
||||
)
|
||||
std_output = gr.Textbox(
|
||||
value=f"Images will be saved at "
|
||||
value=f"{upscaler_model_info}\n"
|
||||
f"Images will be saved at "
|
||||
f"{get_generated_imgs_path()}",
|
||||
lines=1,
|
||||
lines=2,
|
||||
elem_id="std_output",
|
||||
show_label=False,
|
||||
)
|
||||
@@ -605,7 +586,6 @@ with gr.Blocks(title="Upscaler") as upscaler_web:
|
||||
batch_size,
|
||||
scheduler,
|
||||
upscaler_custom_model,
|
||||
upscaler_hf_model_id,
|
||||
custom_vae,
|
||||
precision,
|
||||
device,
|
||||
|
||||
@@ -149,7 +149,6 @@ def import_png_metadata(
|
||||
width,
|
||||
height,
|
||||
custom_model,
|
||||
hf_model_id,
|
||||
custom_lora,
|
||||
hf_lora_id,
|
||||
custom_vae,
|
||||
@@ -175,10 +174,8 @@ def import_png_metadata(
|
||||
|
||||
if "Model" in metadata and png_custom_model:
|
||||
custom_model = png_custom_model
|
||||
hf_model_id = ""
|
||||
if "Model" in metadata and png_hf_model_id:
|
||||
custom_model = "None"
|
||||
hf_model_id = png_hf_model_id
|
||||
elif "Model" in metadata and png_hf_model_id:
|
||||
custom_model = png_hf_model_id
|
||||
|
||||
if "LoRA" in metadata and lora_custom_model:
|
||||
custom_lora = lora_custom_model
|
||||
@@ -217,7 +214,6 @@ def import_png_metadata(
|
||||
width,
|
||||
height,
|
||||
custom_model,
|
||||
hf_model_id,
|
||||
custom_lora,
|
||||
hf_lora_id,
|
||||
custom_vae,
|
||||
|
||||
Reference in New Issue
Block a user