mirror of
https://github.com/sd-webui/stable-diffusion-webui.git
synced 2024-12-14 06:35:14 +03:00
- Improved the layout of the UI.
- Moved relevant options to the top and group them together for easy access.
This commit is contained in:
ZeroCool940711
parent
e5e4954aeb
commit
d940c491a7
@ -394,33 +394,63 @@ def layout():
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sampler_name_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["sampler_name"] = st.selectbox("Sampling method",sampler_name_list,
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index=sampler_name_list.index(st.session_state['defaults'].img2img.sampler_name), help="Sampling method to use.")
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mask_mode_list = ["Mask", "Inverted mask", "Image alpha"]
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mask_mode = st.selectbox("Mask Mode", mask_mode_list,
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help="Select how you want your image to be masked.\"Mask\" modifies the image where the mask is white.\n\
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\"Inverted mask\" modifies the image where the mask is black. \"Image alpha\" modifies the image where the image is transparent."
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)
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mask_mode = mask_mode_list.index(mask_mode)
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index=sampler_name_list.index(st.session_state['defaults'].img2img.sampler_name), help="Sampling method to use.")
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width = st.slider("Width:", min_value=st.session_state['defaults'].img2img.width.min_value, max_value=st.session_state['defaults'].img2img.width.max_value,
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value=st.session_state['defaults'].img2img.width.value, step=st.session_state['defaults'].img2img.width.step)
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value=st.session_state['defaults'].img2img.width.value, step=st.session_state['defaults'].img2img.width.step)
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height = st.slider("Height:", min_value=st.session_state['defaults'].img2img.height.min_value, max_value=st.session_state['defaults'].img2img.height.max_value,
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value=st.session_state['defaults'].img2img.height.value, step=st.session_state['defaults'].img2img.height.step)
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seed = st.text_input("Seed:", value=st.session_state['defaults'].img2img.seed, help=" The seed to use, if left blank a random seed will be generated.")
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noise_mode_list = ["Seed", "Find Noise", "Matched Noise", "Find+Matched Noise"]
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noise_mode = st.selectbox(
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"Noise Mode", noise_mode_list,
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help=""
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)
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noise_mode = noise_mode_list.index(noise_mode)
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find_noise_steps = st.slider("Find Noise Steps", value=st.session_state['defaults'].img2img.find_noise_steps.value,
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min_value=st.session_state['defaults'].img2img.find_noise_steps.min_value, max_value=st.session_state['defaults'].img2img.find_noise_steps.max_value,
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step=st.session_state['defaults'].img2img.find_noise_steps.step)
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batch_count = st.slider("Batch count.", min_value=st.session_state['defaults'].img2img.batch_count.min_value, max_value=st.session_state['defaults'].img2img.batch_count.max_value,
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value=st.session_state['defaults'].img2img.batch_count.value, step=st.session_state['defaults'].img2img.batch_count.step,
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help="How many iterations or batches of images to generate in total.")
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value=st.session_state['defaults'].img2img.height.value, step=st.session_state['defaults'].img2img.height.step)
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seed = st.text_input("Seed:", value=st.session_state['defaults'].img2img.seed, help=" The seed to use, if left blank a random seed will be generated.")
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cfg_scale = st.slider("CFG (Classifier Free Guidance Scale):", min_value=st.session_state['defaults'].img2img.cfg_scale.min_value,
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max_value=st.session_state['defaults'].img2img.cfg_scale.max_value, value=st.session_state['defaults'].img2img.cfg_scale.value,
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step=st.session_state['defaults'].img2img.cfg_scale.step, help="How strongly the image should follow the prompt.")
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st.session_state["denoising_strength"] = st.slider("Denoising Strength:", value=st.session_state['defaults'].img2img.denoising_strength.value,
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min_value=st.session_state['defaults'].img2img.denoising_strength.min_value,
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max_value=st.session_state['defaults'].img2img.denoising_strength.max_value,
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step=st.session_state['defaults'].img2img.denoising_strength.step)
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mask_expander = st.empty()
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with mask_expander.expander("Mask"):
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mask_mode_list = ["Mask", "Inverted mask", "Image alpha"]
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mask_mode = st.selectbox("Mask Mode", mask_mode_list,
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help="Select how you want your image to be masked.\"Mask\" modifies the image where the mask is white.\n\
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\"Inverted mask\" modifies the image where the mask is black. \"Image alpha\" modifies the image where the image is transparent."
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)
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mask_mode = mask_mode_list.index(mask_mode)
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noise_mode_list = ["Seed", "Find Noise", "Matched Noise", "Find+Matched Noise"]
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noise_mode = st.selectbox(
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"Noise Mode", noise_mode_list,
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help=""
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)
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noise_mode = noise_mode_list.index(noise_mode)
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find_noise_steps = st.slider("Find Noise Steps", value=st.session_state['defaults'].img2img.find_noise_steps.value,
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min_value=st.session_state['defaults'].img2img.find_noise_steps.min_value, max_value=st.session_state['defaults'].img2img.find_noise_steps.max_value,
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step=st.session_state['defaults'].img2img.find_noise_steps.step)
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with st.expander("Batch Options"):
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batch_count = st.slider("Batch count.", min_value=st.session_state['defaults'].img2img.batch_count.min_value, max_value=st.session_state['defaults'].img2img.batch_count.max_value,
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value=st.session_state['defaults'].img2img.batch_count.value, step=st.session_state['defaults'].img2img.batch_count.step,
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help="How many iterations or batches of images to generate in total.")
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batch_size = st.slider("Batch size", min_value=st.session_state['defaults'].img2img.batch_size.min_value, max_value=st.session_state['defaults'].img2img.batch_size.max_value,
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value=st.session_state['defaults'].img2img.batch_size.value, step=st.session_state['defaults'].img2img.batch_size.step,
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help="How many images are at once in a batch. It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish \
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generation as more images are generated at once.Default: 1")
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with st.expander("Preview Settings"):
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st.session_state["update_preview"] = st.checkbox("Update Image Preview", value=st.session_state['defaults'].img2img.update_preview,
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help="If enabled the image preview will be updated during the generation instead of at the end. \
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You can use the Update Preview \Frequency option bellow to customize how frequent it's updated. \
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By default this is enabled and the frequency is set to 1 step.")
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st.session_state["update_preview_frequency"] = st.text_input("Update Image Preview Frequency", value=st.session_state['defaults'].img2img.update_preview_frequency,
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help="Frequency in steps at which the the preview image is updated. By default the frequency \
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is set to 1 step.")
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#
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with st.expander("Advanced"):
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separate_prompts = st.checkbox("Create Prompt Matrix.", value=st.session_state['defaults'].img2img.separate_prompts,
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@ -457,31 +487,11 @@ def layout():
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st.session_state["use_RealESRGAN"] = False
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st.session_state["RealESRGAN_model"] = "RealESRGAN_x4plus"
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variant_amount = st.slider("Variant Amount:", value=st.session_state['defaults'].img2img.variant_amount, min_value=0.0, max_value=1.0, step=0.01)
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variant_seed = st.text_input("Variant Seed:", value=st.session_state['defaults'].img2img.variant_seed,
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help="The seed to use when generating a variant, if left blank a random seed will be generated.")
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cfg_scale = st.slider("CFG (Classifier Free Guidance Scale):", min_value=st.session_state['defaults'].img2img.cfg_scale.min_value,
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max_value=st.session_state['defaults'].img2img.cfg_scale.max_value, value=st.session_state['defaults'].img2img.cfg_scale.value,
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step=st.session_state['defaults'].img2img.cfg_scale.step, help="How strongly the image should follow the prompt.")
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batch_size = st.slider("Batch size", min_value=st.session_state['defaults'].img2img.batch_size.min_value, max_value=st.session_state['defaults'].img2img.batch_size.max_value,
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value=st.session_state['defaults'].img2img.batch_size.value, step=st.session_state['defaults'].img2img.batch_size.step,
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help="How many images are at once in a batch. It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish \
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generation as more images are generated at once.Default: 1")
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st.session_state["denoising_strength"] = st.slider("Denoising Strength:", value=st.session_state['defaults'].img2img.denoising_strength.value,
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min_value=st.session_state['defaults'].img2img.denoising_strength.min_value,
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max_value=st.session_state['defaults'].img2img.denoising_strength.max_value,
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step=st.session_state['defaults'].img2img.denoising_strength.step)
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with st.expander("Preview Settings"):
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st.session_state["update_preview"] = st.checkbox("Update Image Preview", value=st.session_state['defaults'].img2img.update_preview,
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help="If enabled the image preview will be updated during the generation instead of at the end. \
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You can use the Update Preview \Frequency option bellow to customize how frequent it's updated. \
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By default this is enabled and the frequency is set to 1 step.")
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st.session_state["update_preview_frequency"] = st.text_input("Update Image Preview Frequency", value=st.session_state['defaults'].img2img.update_preview_frequency,
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help="Frequency in steps at which the the preview image is updated. By default the frequency \
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is set to 1 step.")
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with st.expander("Variant"):
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variant_amount = st.slider("Variant Amount:", value=st.session_state['defaults'].img2img.variant_amount, min_value=0.0, max_value=1.0, step=0.01)
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variant_seed = st.text_input("Variant Seed:", value=st.session_state['defaults'].img2img.variant_seed,
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help="The seed to use when generating a variant, if left blank a random seed will be generated.")
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with col2_img2img_layout:
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editor_tab = st.tabs(["Editor"])
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@ -510,6 +520,7 @@ def layout():
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help="Upload an mask image which will be used for masking the image to image generation.",
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)
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if uploaded_masks:
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mask_expander.expander("Mask", expanded=True)
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mask = Image.open(uploaded_masks)
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if mask.mode == "RGBA":
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mask = mask.convert('RGBA')
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@ -164,15 +164,17 @@ def layout():
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value=st.session_state['defaults'].txt2img.cfg_scale.value, step=st.session_state['defaults'].txt2img.cfg_scale.step,
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help="How strongly the image should follow the prompt.")
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seed = st.text_input("Seed:", value=st.session_state['defaults'].txt2img.seed, help=" The seed to use, if left blank a random seed will be generated.")
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batch_count = st.slider("Batch count.", min_value=st.session_state['defaults'].txt2img.batch_count.min_value, max_value=st.session_state['defaults'].txt2img.batch_count.max_value,
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value=st.session_state['defaults'].txt2img.batch_count.value, step=st.session_state['defaults'].txt2img.batch_count.step,
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help="How many iterations or batches of images to generate in total.")
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batch_size = st.slider("Batch size", min_value=st.session_state['defaults'].txt2img.batch_size.min_value, max_value=st.session_state['defaults'].txt2img.batch_size.max_value,
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value=st.session_state.defaults.txt2img.batch_size.value, step=st.session_state.defaults.txt2img.batch_size.step,
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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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Default: 1")
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with st.expander("Batch Options"):
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batch_count = st.slider("Batch count.", min_value=st.session_state['defaults'].txt2img.batch_count.min_value, max_value=st.session_state['defaults'].txt2img.batch_count.max_value,
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value=st.session_state['defaults'].txt2img.batch_count.value, step=st.session_state['defaults'].txt2img.batch_count.step,
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help="How many iterations or batches of images to generate in total.")
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batch_size = st.slider("Batch size", min_value=st.session_state['defaults'].txt2img.batch_size.min_value, max_value=st.session_state['defaults'].txt2img.batch_size.max_value,
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value=st.session_state.defaults.txt2img.batch_size.value, step=st.session_state.defaults.txt2img.batch_size.step,
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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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Default: 1")
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with st.expander("Preview Settings"):
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st.session_state["update_preview"] = st.checkbox("Update Image Preview", value=st.session_state['defaults'].txt2img.update_preview,
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@ -257,12 +259,14 @@ def layout():
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else:
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st.session_state["use_RealESRGAN"] = False
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st.session_state["RealESRGAN_model"] = "RealESRGAN_x4plus"
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variant_amount = st.slider("Variant Amount:", value=st.session_state['defaults'].txt2img.variant_amount.value,
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min_value=st.session_state['defaults'].txt2img.variant_amount.min_value, max_value=st.session_state['defaults'].txt2img.variant_amount.max_value,
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step=st.session_state['defaults'].txt2img.variant_amount.step)
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variant_seed = st.text_input("Variant Seed:", value=st.session_state['defaults'].txt2img.seed, help="The seed to use when generating a variant, if left blank a random seed will be generated.")
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galleryCont = st.empty()
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with st.expander("Variant"):
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variant_amount = st.slider("Variant Amount:", value=st.session_state['defaults'].txt2img.variant_amount.value,
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min_value=st.session_state['defaults'].txt2img.variant_amount.min_value, max_value=st.session_state['defaults'].txt2img.variant_amount.max_value,
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step=st.session_state['defaults'].txt2img.variant_amount.step)
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variant_seed = st.text_input("Variant Seed:", value=st.session_state['defaults'].txt2img.seed, help="The seed to use when generating a variant, if left blank a random seed will be generated.")
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#galleryCont = st.empty()
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if generate_button:
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#print("Loading models")
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@ -271,51 +275,51 @@ def layout():
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st.session_state["custom_model"])
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try:
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#try:
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#
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output_images, seeds, info, stats = txt2img(prompt, st.session_state.sampling_steps, sampler_name, st.session_state["RealESRGAN_model"], batch_count, batch_size,
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cfg_scale, seed, height, width, separate_prompts, normalize_prompt_weights, save_individual_images,
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save_grid, group_by_prompt, save_as_jpg, st.session_state["use_GFPGAN"], st.session_state["use_RealESRGAN"], st.session_state["RealESRGAN_model"],
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variant_amount=variant_amount, variant_seed=variant_seed, write_info_files=write_info_files)
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output_images, seeds, info, stats = txt2img(prompt, st.session_state.sampling_steps, sampler_name, st.session_state["RealESRGAN_model"], batch_count, batch_size,
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cfg_scale, seed, height, width, separate_prompts, normalize_prompt_weights, save_individual_images,
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save_grid, group_by_prompt, save_as_jpg, st.session_state["use_GFPGAN"], st.session_state["use_RealESRGAN"], st.session_state["RealESRGAN_model"],
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variant_amount=variant_amount, variant_seed=variant_seed, write_info_files=write_info_files)
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message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")
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#history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont = st.session_state['historyTab']
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#if 'latestImages' in st.session_state:
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#for i in output_images:
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##push the new image to the list of latest images and remove the oldest one
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##remove the last index from the list\
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#st.session_state['latestImages'].pop()
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##add the new image to the start of the list
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#st.session_state['latestImages'].insert(0, i)
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#PlaceHolder.empty()
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#with PlaceHolder.container():
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#col1, col2, col3 = st.columns(3)
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#col1_cont = st.container()
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#col2_cont = st.container()
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#col3_cont = st.container()
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#images = st.session_state['latestImages']
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#with col1_cont:
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#with col1:
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#[st.image(images[index]) for index in [0, 3, 6] if index < len(images)]
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#with col2_cont:
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#with col2:
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#[st.image(images[index]) for index in [1, 4, 7] if index < len(images)]
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#with col3_cont:
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#with col3:
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#[st.image(images[index]) for index in [2, 5, 8] if index < len(images)]
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#historyGallery = st.empty()
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## check if output_images length is the same as seeds length
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#with gallery_tab:
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#st.markdown(createHTMLGallery(output_images,seeds), unsafe_allow_html=True)
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message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")
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#history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont = st.session_state['historyTab']
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#if 'latestImages' in st.session_state:
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#for i in output_images:
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##push the new image to the list of latest images and remove the oldest one
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##remove the last index from the list\
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#st.session_state['latestImages'].pop()
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##add the new image to the start of the list
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#st.session_state['latestImages'].insert(0, i)
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#PlaceHolder.empty()
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#with PlaceHolder.container():
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#col1, col2, col3 = st.columns(3)
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#col1_cont = st.container()
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#col2_cont = st.container()
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#col3_cont = st.container()
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#images = st.session_state['latestImages']
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#with col1_cont:
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#with col1:
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#[st.image(images[index]) for index in [0, 3, 6] if index < len(images)]
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#with col2_cont:
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#with col2:
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#[st.image(images[index]) for index in [1, 4, 7] if index < len(images)]
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#with col3_cont:
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#with col3:
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#[st.image(images[index]) for index in [2, 5, 8] if index < len(images)]
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#historyGallery = st.empty()
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## check if output_images length is the same as seeds length
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#with gallery_tab:
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#st.markdown(createHTMLGallery(output_images,seeds), unsafe_allow_html=True)
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#st.session_state['historyTab'] = [history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont]
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#st.session_state['historyTab'] = [history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont]
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except (StopException, KeyError):
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print(f"Received Streamlit StopException")
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#except (StopException, KeyError):
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#print(f"Received Streamlit StopException")
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# this will render all the images at the end of the generation but its better if its moved to a second tab inside col2 and shown as a gallery.
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# use the current col2 first tab to show the preview_img and update it as its generated.
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