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https://github.com/sd-webui/stable-diffusion-webui.git
synced 2024-12-13 18:02:31 +03:00
Fixed multiple settings and default values not working properly.
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@ -29,11 +29,11 @@ general:
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default_model_path: "models/ldm/stable-diffusion-v1/model.ckpt"
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use_sd_concepts_library: True
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sd_concepts_library_folder: "models/custom/sd-concepts-library"
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GFPGAN_dir: "./src/gfpgan"
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GFPGAN_dir: "./models/gfpgan"
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GFPGAN_model: "GFPGANv1.4"
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LDSR_dir: "./models/ldsr"
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LDSR_model: "model"
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RealESRGAN_dir: "./src/realesrgan"
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RealESRGAN_dir: "./models/realesrgan"
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RealESRGAN_model: "RealESRGAN_x4plus"
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upscaling_method: "RealESRGAN"
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outdir_txt2img: outputs/txt2img
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@ -91,6 +91,9 @@ txt2img:
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sampling_steps:
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value: 30
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min_value: 10
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max_value: 250
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step: 10
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LDSR_config:
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sampling_steps: 50
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@ -282,14 +282,14 @@ def layout():
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st.title("General Parameters")
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# Batch Count
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st.session_state["batch_count"] = st.text_input("Batch count.", value=st.session_state['defaults'].txt2img.batch_count.value,
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help="How many iterations or batches of images to generate in total.")
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st.session_state["defaults"].txt2img.batch_count.value = int(st.text_input("Batch count", value=st.session_state['defaults'].txt2img.batch_count.value,
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help="How many iterations or batches of images to generate in total."))
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st.session_state["batch_size"] = st.text_input("Batch size", value=st.session_state.defaults.txt2img.batch_size.value,
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st.session_state["defaults"].txt2img.batch_size.value = int(st.text_input("Batch size", value=st.session_state.defaults.txt2img.batch_size.value,
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help="How many images are at once in a batch.\
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It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
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takes to finish generation as more images are generated at once.\
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Default: 1")
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Default: 1"))
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default_sampler_list = ["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "k_heun", "PLMS", "DDIM"]
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st.session_state["defaults"].txt2img.default_sampler = st.selectbox("Default Sampler",
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@ -446,14 +446,14 @@ def layout():
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help="Set the default value for the number of steps on the sampling steps slider. Default is: 10"))
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# Batch Count
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st.session_state["batch_count"] = st.text_input("Batch count.", value=st.session_state['defaults'].txt2img.batch_count.value,
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help="How many iterations or batches of images to generate in total.")
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st.session_state["defaults"].img2img.batch_count.value = int(st.text_input("Img2img Batch count", value=st.session_state["defaults"].img2img.batch_count.value,
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help="How many iterations or batches of images to generate in total."))
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st.session_state["batch_size"] = st.text_input("Batch size", value=st.session_state.defaults.txt2img.batch_size.value,
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st.session_state["defaults"].img2img.batch_size.value = int(st.text_input("Img2img Batch size", value=st.session_state["defaults"].img2img.batch_size.value,
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help="How many images are at once in a batch.\
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It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
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takes to finish generation as more images are generated at once.\
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Default: 1")
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Default: 1"))
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with col4:
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# Inference Steps
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st.session_state["defaults"].img2img.num_inference_steps.value = int(st.text_input("Default Inference Steps",
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@ -635,14 +635,14 @@ def layout():
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help="Set the default value for the number of steps on the sampling steps slider. Default is: 10"))
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# Batch Count
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st.session_state["batch_count"] = st.text_input("Batch count.", value=st.session_state['defaults'].txt2img.batch_count.value,
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help="How many iterations or batches of images to generate in total.")
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st.session_state["defaults"].txt2vid.batch_count.value = int(st.text_input("txt2vid Batch count", value=st.session_state['defaults'].txt2vid.batch_count.value,
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help="How many iterations or batches of images to generate in total."))
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st.session_state["batch_size"] = st.text_input("Batch size", value=st.session_state.defaults.txt2img.batch_size.value,
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st.session_state["defaults"].txt2vid.batch_size.value = int(st.text_input("txt2vid Batch size", value=st.session_state.defaults.txt2vid.batch_size.value,
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help="How many images are at once in a batch.\
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It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
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takes to finish generation as more images are generated at once.\
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Default: 1")
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Default: 1") )
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# Inference Steps
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st.session_state["defaults"].txt2vid.num_inference_steps.value = int(st.text_input("Default Txt2Vid Inference Steps",
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@ -215,7 +215,8 @@ def layout():
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st.session_state["batch_size"] = int(st.text_input("Batch size", value=st.session_state.defaults.txt2img.batch_size.value,
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help="How many images are at once in a batch.\
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It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\
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It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes \
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to finish generation as more images are generated at once.\
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Default: 1") )
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with st.expander("Preview Settings"):
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@ -336,8 +337,9 @@ def layout():
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if st.session_state["LDSR_available"]:
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upscaling_method_list.append("LDSR")
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#print (st.session_state["RealESRGAN_available"])
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st.session_state["upscaling_method"] = st.selectbox("Upscaling Method", upscaling_method_list,
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index=upscaling_method_list.index(st.session_state['defaults'].general.upscaling_method))
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index=upscaling_method_list.index(str(st.session_state['defaults'].general.upscaling_method)))
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if st.session_state["RealESRGAN_available"]:
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with st.expander("RealESRGAN"):
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