(
state,
version,
version_dict,
is_legacy=False,
return_latents=False,
filter=None,
stage2strength=None,
)
| 110 | |
| 111 | |
| 112 | def run_txt2img( |
| 113 | state, |
| 114 | version, |
| 115 | version_dict, |
| 116 | is_legacy=False, |
| 117 | return_latents=False, |
| 118 | filter=None, |
| 119 | stage2strength=None, |
| 120 | ): |
| 121 | if version.startswith("SDXL-base"): |
| 122 | W, H = st.selectbox("Resolution:", list(SD_XL_BASE_RATIOS.values()), 10) |
| 123 | else: |
| 124 | H = st.number_input("H", value=version_dict["H"], min_value=64, max_value=2048) |
| 125 | W = st.number_input("W", value=version_dict["W"], min_value=64, max_value=2048) |
| 126 | C = version_dict["C"] |
| 127 | F = version_dict["f"] |
| 128 | |
| 129 | init_dict = { |
| 130 | "orig_width": W, |
| 131 | "orig_height": H, |
| 132 | "target_width": W, |
| 133 | "target_height": H, |
| 134 | } |
| 135 | value_dict = init_embedder_options( |
| 136 | get_unique_embedder_keys_from_conditioner(state["model"].conditioner), |
| 137 | init_dict, |
| 138 | prompt=prompt, |
| 139 | negative_prompt=negative_prompt, |
| 140 | ) |
| 141 | sampler, num_rows, num_cols = init_sampling(stage2strength=stage2strength) |
| 142 | num_samples = num_rows * num_cols |
| 143 | |
| 144 | if st.button("Sample"): |
| 145 | st.write(f"**Model I:** {version}") |
| 146 | out = do_sample( |
| 147 | state["model"], |
| 148 | sampler, |
| 149 | value_dict, |
| 150 | num_samples, |
| 151 | H, |
| 152 | W, |
| 153 | C, |
| 154 | F, |
| 155 | force_uc_zero_embeddings=["txt"] if not is_legacy else [], |
| 156 | return_latents=return_latents, |
| 157 | filter=filter, |
| 158 | ) |
| 159 | return out |
| 160 | |
| 161 | |
| 162 | def run_img2img( |
no test coverage detected