Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-08-29 08:31:00 +02:00
Code Issues Packages Projects Releases Wiki Activity

Merge pull request #2128 from vladmandic/master

Browse Source 
refresh dev
This commit is contained in:
Vladimir Mandic
2023-09-03 15:59:45 -04:00
committed by GitHub
parent 13c2638bcd 1d04014df1
commit 8258313555
5 changed files with 107 additions and 78 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/processing_diffusers.py
+16 -5
View File
@@ -268,9 +268,19 @@ 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) 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:
return int(p.steps // p.denoising_strength + 1)
else:
return p.steps
base_args = set_pipeline_args(
model=shared.sd_model,
@@ -278,7 +288,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
negative_prompts=negative_prompts,
prompts_2=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else prompts,
negative_prompts_2=[p.refiner_negative] if len(p.refiner_negative) > 0 else negative_prompts,
num_inference_steps=int(p.steps // p.refiner_start + 1) if use_refiner_start else int(p.steps // (1 - p.refiner_start)) if use_denoise_start else int(p.steps // p.denoising_strength + 1) if is_img2img else p.steps,
num_inference_steps=calculate_base_steps(),
eta=shared.opts.eta_ddim,
guidance_rescale=p.diffusers_guidance_rescale,
denoising_start=0 if use_refiner_start else p.refiner_start if use_denoise_start else None,
@@ -355,13 +365,14 @@ 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,