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simplify processing call

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This commit is contained in:
Vladimir Mandic
2023-12-27 20:24:41 -05:00
parent 1a0e3c6bee
commit 8944d3bdfa
2 changed files with 18 additions and 17 deletions
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modules/processing_diffusers.py
+17 -16
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@@ -24,7 +24,8 @@ debug_steps = shared.log.trace if os.environ.get('SD_STEPS_DEBUG', None) is not
debug_steps('Trace: STEPS')
def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_prompts):
def process_diffusers(p: StableDiffusionProcessing):
debug(f'Process diffusers args: {vars(p)}')
results = []
def is_txt2img():
@@ -71,7 +72,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
info=create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, [], iteration=p.iteration, position_in_batch=i)
decoded = vae_decode(latents=latents, model=shared.sd_model, output_type='pil', full_quality=p.full_quality)
for j in range(len(decoded)):
images.save_image(decoded[j], path=p.outpath_samples, basename="", seed=seeds[i], prompt=prompts[i], extension=shared.opts.samples_format, info=info, p=p, suffix=suffix)
images.save_image(decoded[j], path=p.outpath_samples, basename="", seed=p.seeds[i], prompt=p.prompts[i], extension=shared.opts.samples_format, info=info, p=p, suffix=suffix)
def diffusers_callback_legacy(step: int, timestep: int, latents: torch.FloatTensor):
shared.state.sampling_step = step
@@ -218,7 +219,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
generator = None
else:
generator_device = devices.cpu if shared.opts.diffusers_generator_device == "CPU" else shared.device
generator = [torch.Generator(generator_device).manual_seed(s) for s in seeds]
generator = [torch.Generator(generator_device).manual_seed(s) for s in p.seeds]
prompts, negative_prompts, prompts_2, negative_prompts_2 = fix_prompts(prompts, negative_prompts, prompts_2, negative_prompts_2)
parser = 'Fixed attention'
if shared.opts.prompt_attention != 'Fixed attention' and 'StableDiffusion' in model.__class__.__name__:
@@ -246,9 +247,9 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
args['negative_prompt'] = negative_prompts
if hasattr(model, 'scheduler') and hasattr(model.scheduler, 'noise_sampler_seed') and hasattr(model.scheduler, 'noise_sampler'):
model.scheduler.noise_sampler = None # noise needs to be reset instead of using cached values
model.scheduler.noise_sampler_seed = seeds[0] # some schedulers have internal noise generator and do not use pipeline generator
model.scheduler.noise_sampler_seed = p.seeds[0] # some schedulers have internal noise generator and do not use pipeline generator
if 'noise_sampler_seed' in possible:
args['noise_sampler_seed'] = seeds[0]
args['noise_sampler_seed'] = p.seeds[0]
if 'guidance_scale' in possible:
args['guidance_scale'] = p.cfg_scale
if 'generator' in possible and generator is not None:
@@ -378,7 +379,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
# p.extra_generation_params['Sampler options'] = ''
if len(getattr(p, 'init_images', [])) > 0:
while len(p.init_images) < len(prompts):
while len(p.init_images) < len(p.prompts):
p.init_images.append(p.init_images[-1])
if shared.state.interrupted or shared.state.skipped:
@@ -441,10 +442,10 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
base_args = set_pipeline_args(
model=shared.sd_model,
prompts=prompts,
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,
prompts=p.prompts,
negative_prompts=p.negative_prompts,
prompts_2=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else p.prompts,
negative_prompts_2=[p.refiner_negative] if len(p.refiner_negative) > 0 else p.negative_prompts,
num_inference_steps=calculate_base_steps(),
eta=shared.opts.scheduler_eta,
guidance_scale=p.cfg_scale,
@@ -510,10 +511,10 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
update_sampler(shared.sd_model, second_pass=True)
hires_args = set_pipeline_args(
model=shared.sd_model,
prompts=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else prompts,
negative_prompts=[p.refiner_negative] if len(p.refiner_negative) > 0 else 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,
prompts=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else p.prompts,
negative_prompts=[p.refiner_negative] if len(p.refiner_negative) > 0 else p.negative_prompts,
prompts_2=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else p.prompts,
negative_prompts_2=[p.refiner_negative] if len(p.refiner_negative) > 0 else p.negative_prompts,
num_inference_steps=calculate_hires_steps(),
eta=shared.opts.scheduler_eta,
guidance_scale=p.image_cfg_scale if p.image_cfg_scale is not None else p.cfg_scale,
@@ -569,8 +570,8 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
output_type = 'np'
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],
prompts=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else p.prompts[i],
negative_prompts=[p.refiner_negative] if len(p.refiner_negative) > 0 else p.negative_prompts[i],
num_inference_steps=calculate_refiner_steps(),
eta=shared.opts.scheduler_eta,
# strength=p.denoising_strength,