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slider_steps and slider_bounds in defaults config
slider_steps and slider_bounds in defaults config
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hlky
parent
74fd533077
commit
6f4a1d8a41
@ -62,6 +62,15 @@ txt2img:
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variant_amount: 0.0
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variant_seed: ""
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write_info_files: True
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slider_steps: {
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sampling: 1
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}
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slider_bounds: {
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sampling: {
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lower: 1,
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upper: 150
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}
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}
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txt2vid:
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default_model: "CompVis/stable-diffusion-v1-4"
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@ -97,58 +106,76 @@ txt2vid:
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beta_end: 0.012
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beta_scheduler_type: "linear"
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max_frames: 1000
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slider_steps: {
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sampling: 1
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}
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slider_bounds: {
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sampling: {
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lower: 1,
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upper: 150
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}
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}
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img2img:
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prompt:
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sampling_steps: 30
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# Adding an int to toggles enables the corresponding feature.
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# 0: Create prompt matrix (separate multiple prompts using |, and get all combinations of them)
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# 1: Normalize Prompt Weights (ensure sum of weights add up to 1.0)
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# 2: Loopback (use images from previous batch when creating next batch)
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# 3: Random loopback seed
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# 4: Save individual images
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# 5: Save grid
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# 6: Sort samples by prompt
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# 7: Write sample info files
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# 8: jpg samples
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# 9: Fix faces using GFPGAN
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# 10: Upscale images using Real-ESRGAN
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sampler_name: "k_euler"
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denoising_strength: 0.75
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# 0: Keep masked area
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# 1: Regenerate only masked area
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mask_mode: 0
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mask_restore: False
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# 0: Just resize
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# 1: Crop and resize
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# 2: Resize and fill
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resize_mode: 0
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# Leave blank for random seed:
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seed: ""
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ddim_eta: 0.0
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cfg_scale: 7.5
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batch_count: 1
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batch_size: 1
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height: 512
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width: 512
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# Textual inversion embeddings file path:
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fp: ""
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loopback: True
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random_seed_loopback: True
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separate_prompts: False
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update_preview: True
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update_preview_frequency: 5
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normalize_prompt_weights: True
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save_individual_images: True
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save_grid: True
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group_by_prompt: True
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save_as_jpg: False
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use_GFPGAN: False
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use_RealESRGAN: False
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RealESRGAN_model: "RealESRGAN_x4plus"
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variant_amount: 0.0
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variant_seed: ""
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write_info_files: True
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prompt:
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sampling_steps: 30
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# Adding an int to toggles enables the corresponding feature.
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# 0: Create prompt matrix (separate multiple prompts using |, and get all combinations of them)
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# 1: Normalize Prompt Weights (ensure sum of weights add up to 1.0)
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# 2: Loopback (use images from previous batch when creating next batch)
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# 3: Random loopback seed
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# 4: Save individual images
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# 5: Save grid
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# 6: Sort samples by prompt
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# 7: Write sample info files
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# 8: jpg samples
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# 9: Fix faces using GFPGAN
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# 10: Upscale images using Real-ESRGAN
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sampler_name: "k_euler"
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denoising_strength: 0.75
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# 0: Keep masked area
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# 1: Regenerate only masked area
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mask_mode: 0
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mask_restore: False
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# 0: Just resize
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# 1: Crop and resize
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# 2: Resize and fill
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resize_mode: 0
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# Leave blank for random seed:
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seed: ""
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ddim_eta: 0.0
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cfg_scale: 7.5
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batch_count: 1
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batch_size: 1
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height: 512
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width: 512
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# Textual inversion embeddings file path:
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fp: ""
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loopback: True
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random_seed_loopback: True
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separate_prompts: False
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update_preview: True
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update_preview_frequency: 5
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normalize_prompt_weights: True
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save_individual_images: True
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save_grid: True
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group_by_prompt: True
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save_as_jpg: False
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use_GFPGAN: False
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use_RealESRGAN: False
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RealESRGAN_model: "RealESRGAN_x4plus"
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variant_amount: 0.0
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variant_seed: ""
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write_info_files: True
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slider_steps: {
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sampling: 1
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}
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slider_bounds: {
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sampling: {
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lower: 1,
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upper: 150
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}
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}
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gfpgan:
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strength: 100
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@ -383,7 +383,11 @@ def layout():
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st.session_state["custom_model"] = "Stable Diffusion v1.4"
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st.session_state["sampling_steps"] = st.slider("Sampling Steps", value=st.session_state['defaults'].img2img.sampling_steps, min_value=1, max_value=500)
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st.session_state["sampling_steps"] = st.slider("Sampling Steps",
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value=st.session_state['defaults'].img2img.sampling_steps,
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min_value=st.session_state['defaults'].img2img.slider_bounds.sampling.lower,
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max_value=st.session_state['defaults'].img2img.slider_bounds.sampling.upper,
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step=st.session_state['defaults'].img2img.slider_steps.sampling)
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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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@ -217,7 +217,11 @@ def layout():
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the file for the model has on said folder, it is recommended to give the .ckpt file a name that \
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will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.4")
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st.session_state.sampling_steps = st.slider("Sampling Steps", value=st.session_state['defaults'].txt2img.sampling_steps, min_value=10, max_value=500, step=10)
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st.session_state.sampling_steps = st.slider("Sampling Steps",
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value=st.session_state['defaults'].txt2img.sampling_steps,
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min_value=st.session_state['defaults'].txt2img.slider_bounds.sampling.lower,
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max_value=st.session_state['defaults'].txt2img.slider_bounds.sampling.upper,
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step=st.session_state['defaults'].txt2img.slider_steps.sampling)
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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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sampler_name = st.selectbox("Sampling method", sampler_name_list,
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@ -650,8 +650,12 @@ def layout():
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#custom_model = "CompVis/stable-diffusion-v1-4"
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#st.session_state["weights_path"] = f"CompVis/{slugify(custom_model.lower())}"
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st.session_state.sampling_steps = st.slider("Sampling Steps", value=st.session_state['defaults'].txt2vid.sampling_steps, min_value=10, step=10, max_value=500,
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help="Number of steps between each pair of sampled points")
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st.session_state.sampling_steps = st.slider("Sampling Steps",
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value=st.session_state['defaults'].txt2vid.sampling_steps,
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min_value=st.session_state['defaults'].txt2vid.slider_bounds.sampling.lower,
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max_value=st.session_state['defaults'].txt2vid.slider_bounds.sampling.upper,
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step=st.session_state['defaults'].txt2vid.slider_steps.sampling,
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help="Number of steps between each pair of sampled points")
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st.session_state.num_inference_steps = st.slider("Inference Steps:", value=st.session_state['defaults'].txt2vid.num_inference_steps, min_value=10,step=10, max_value=500,
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help="Higher values (e.g. 100, 200 etc) can create better images.")
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