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Fix inflated steps with Non SDXL Refiner

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Disty0
2023-09-02 17:25:31 +03:00
parent 3287222177
commit 1d04014df1
1 changed files with 6 additions and 5 deletions
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modules/processing_diffusers.py
+6 -5
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@@ -261,13 +261,13 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
if shared.opts.diffusers_move_base and not shared.sd_model.has_accelerate:
shared.sd_model.to(devices.device)
is_img2img = (sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.IMAGE_2_IMAGE or sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.INPAINTING)
use_refiner_start = (is_refiner_enabled and not p.is_hr_pass and not is_img2img and p.refiner_start > 0 and p.refiner_start < 1)
use_denoise_start = (is_img2img and p.refiner_start > 0 and p.refiner_start < 1)
is_img2img = bool(sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.IMAGE_2_IMAGE or sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.INPAINTING)
use_refiner_start = bool(is_refiner_enabled and not p.is_hr_pass and not is_img2img and p.refiner_start > 0 and p.refiner_start < 1)
use_denoise_start = bool(is_img2img and p.refiner_start > 0 and p.refiner_start < 1)
def calculate_base_steps():
if use_refiner_start:
return int(p.steps // p.refiner_start + 1)
return int(p.steps // p.refiner_start + 1) if shared.sd_model_type == 'sdxl' else p.steps
elif use_denoise_start and shared.sd_model_type == 'sdxl':
return int(p.steps // (1 - p.refiner_start))
elif is_img2img:
@@ -358,13 +358,14 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
if shared.opts.diffusers_move_refiner and not shared.sd_refiner.has_accelerate:
shared.sd_refiner.to(devices.device)
refiner_is_sdxl = bool("StableDiffusionXL" in shared.sd_refiner.__class__.__name__)
p.ops.append('refine')
for i in range(len(output.images)):
refiner_args = set_pipeline_args(
model=shared.sd_refiner,
prompts=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else prompts[i],
negative_prompts=[p.refiner_negative] if len(p.refiner_negative) > 0 else negative_prompts[i],
num_inference_steps=int(p.refiner_steps // (1 - p.refiner_start)) if p.refiner_start > 0 and p.refiner_start < 1 else int(p.refiner_steps // p.denoising_strength + 1),
num_inference_steps=int(p.refiner_steps // (1 - p.refiner_start)) if p.refiner_start > 0 and p.refiner_start < 1 and refiner_is_sdxl else int(p.refiner_steps // p.denoising_strength + 1) if refiner_is_sdxl else p.refiner_steps,
eta=shared.opts.eta_ddim,
strength=p.denoising_strength,
guidance_scale=p.image_cfg_scale if p.image_cfg_scale is not None else p.cfg_scale,