lllyasviel/stable-diffusion-webui-forge
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batch support

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lllyasviel
2024-02-04 22:04:20 -08:00
parent c6f7fa82ff
commit a8c43f7af0
1 changed files with 44 additions and 26 deletions
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70
extensions-builtin/sd_forge_controlnet/scripts/controlnet.py
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@@ -251,42 +251,60 @@ class ControlNetForForgeOfficial(scripts.Script):
preprocessor = global_state.get_preprocessor(unit.module) preprocessor = global_state.get_preprocessor(unit.module)
input_list, resize_mode = self.get_input_data(p, unit, preprocessor) input_list, resize_mode = self.get_input_data(p, unit, preprocessor)
preprocessor_outputs = []
preprocessor_output_is_image = False
input_image, input_mask = input_list[0] input_image, input_mask = input_list[0]
# p.extra_result_images.append(input_image) preprocessor_output = None
if unit.pixel_perfect: for input_image, input_mask in input_list:
unit.processor_res = external_code.pixel_perfect_resolution( # p.extra_result_images.append(input_image)
input_image,
target_H=h, if unit.pixel_perfect:
target_W=w, unit.processor_res = external_code.pixel_perfect_resolution(
resize_mode=resize_mode, input_image,
target_H=h,
target_W=w,
resize_mode=resize_mode,
)
seed = set_numpy_seed(p)
logger.debug(f"Use numpy seed {seed}.")
logger.info(f"Using preprocessor: {unit.module}")
logger.info(f'preprocessor resolution = {unit.processor_res}')
preprocessor_output = preprocessor(
input_image=input_image,
input_mask=input_mask,
resolution=unit.processor_res,
slider_1=unit.threshold_a,
slider_2=unit.threshold_b,
) )
seed = set_numpy_seed(p) preprocessor_outputs.append(preprocessor_output)
logger.debug(f"Use numpy seed {seed}.")
logger.info(f"Using preprocessor: {unit.module}")
logger.info(f'preprocessor resolution = {unit.processor_res}')
preprocessor_output = preprocessor( preprocessor_output_is_image = judge_image_type(preprocessor_output)
input_image=input_image,
input_mask=input_mask,
resolution=unit.processor_res,
slider_1=unit.threshold_a,
slider_2=unit.threshold_b,
)
preprocessor_output_is_image = judge_image_type(preprocessor_output) if len(input_list) > 1 and not preprocessor_output_is_image:
logger.info('Batch wise input only support controlnet, control-lora, and t2i adapters!')
break
if preprocessor_output_is_image: if preprocessor_output_is_image:
params.control_cond = crop_and_resize_image(preprocessor_output, resize_mode, h, w) params.control_cond = []
p.extra_result_images.append(external_code.visualize_inpaint_mask(params.control_cond)) params.control_cond_for_hr_fix = []
params.control_cond = numpy_to_pytorch(params.control_cond).movedim(-1, 1)
for preprocessor_output in preprocessor_outputs:
control_cond = crop_and_resize_image(preprocessor_output, resize_mode, h, w)
p.extra_result_images.append(external_code.visualize_inpaint_mask(control_cond))
params.control_cond.append(numpy_to_pytorch(control_cond).movedim(-1, 1))
params.control_cond = torch.cat(params.control_cond, dim=0)
if has_high_res_fix: if has_high_res_fix:
params.control_cond_for_hr_fix = crop_and_resize_image(preprocessor_output, resize_mode, hr_y, hr_x) for preprocessor_output in preprocessor_outputs:
p.extra_result_images.append(external_code.visualize_inpaint_mask(params.control_cond_for_hr_fix)) control_cond_for_hr_fix = crop_and_resize_image(preprocessor_output, resize_mode, hr_y, hr_x)
params.control_cond_for_hr_fix = numpy_to_pytorch(params.control_cond_for_hr_fix).movedim(-1, 1) p.extra_result_images.append(external_code.visualize_inpaint_mask(control_cond_for_hr_fix))
params.control_cond_for_hr_fix.append(numpy_to_pytorch(control_cond_for_hr_fix).movedim(-1, 1))
params.control_cond_for_hr_fix = torch.cat(params.control_cond_for_hr_fix, dim=0)
else: else:
params.control_cond_for_hr_fix = params.control_cond params.control_cond_for_hr_fix = params.control_cond
else: else: