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refactor stable-cascade, fix taesd bf16, add skip-env cmd flag

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Vladimir Mandic
2024-02-21 17:13:53 -05:00
parent aec8f76c15
commit cf7118be4d
16 changed files with 100 additions and 51 deletions
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modules/processing_diffusers.py
+38 -25
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@@ -165,7 +165,7 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
if hasattr(model, 'pipe'): # recurse
model = model.pipe
signature = inspect.signature(type(model).__call__, follow_wrapped=True)
possible = signature.parameters.keys()
possible = list(signature.parameters)
debug(f'Diffusers pipeline possible: {possible}')
if shared.opts.diffusers_generator_device == "Unset":
generator_device = None
@@ -179,15 +179,16 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
prompts, negative_prompts, prompts_2, negative_prompts_2 = fix_prompts(prompts, negative_prompts, prompts_2, negative_prompts_2)
parser = 'Fixed attention'
clip_skip = kwargs.pop("clip_skip", 1)
steps = kwargs.get("num_inference_steps", 1)
if shared.opts.prompt_attention != 'Fixed attention' and 'StableDiffusion' in model.__class__.__name__ and 'Onnx' not in model.__class__.__name__:
try:
prompt_parser_diffusers.encode_prompts(model, p, prompts, negative_prompts, steps=kwargs.get("num_inference_steps", 1), clip_skip=clip_skip)
prompt_parser_diffusers.encode_prompts(model, p, prompts, negative_prompts, steps=steps, clip_skip=clip_skip)
parser = shared.opts.prompt_attention
except Exception as e:
shared.log.error(f'Prompt parser encode: {e}')
if os.environ.get('SD_PROMPT_DEBUG', None) is not None:
errors.display(e, 'Prompt parser encode')
if parser == 'Fixed attention':
if 'clip_skip' in possible and parser == 'Fixed attention':
if clip_skip == 1:
pass # clip_skip = None
else:
@@ -211,17 +212,7 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
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'] = p.seeds[0]
if hasattr(model, "decoder") and hasattr(model, "prior_prior") and 'prior_num_inference_steps' in possible:
steps = kwargs.pop("num_inference_steps", 20)
args["prior_num_inference_steps"] = steps
args["num_inference_steps"] = max(int(steps / 2), 1)
if hasattr(model, "decoder") and hasattr(model, "prior_prior") and 'prior_guidance_scale' in possible:
cfg_scale = kwargs.pop("guidance_scale", p.cfg_scale)
args["prior_guidance_scale"] = cfg_scale
# Using decoder_guidance_scale causes "Expected all tensors to be on the same device" errors right now
# Enabling model cpu offload fixes the error above
#args["decoder_guidance_scale"] = 0.0
elif 'guidance_scale' in possible:
if 'guidance_scale' in possible:
args['guidance_scale'] = p.cfg_scale
if 'generator' in possible and generator is not None:
args['generator'] = generator
@@ -230,18 +221,40 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
if 'output_type' in possible:
if hasattr(model, 'vae'):
args['output_type'] = 'np' # only set latent if model has vae
# stable cascade
if 'StableCascade' in model.__class__.__name__:
kwargs.pop("guidance_scale") # remove
kwargs.pop("num_inference_steps") # remove
if 'prior_num_inference_steps' in possible:
args["prior_num_inference_steps"] = p.steps
args["num_inference_steps"] = p.refiner_steps
if 'prior_guidance_scale' in possible:
args["prior_guidance_scale"] = p.cfg_scale
if 'decoder_guidance_scale' in possible:
args["decoder_guidance_scale"] = p.image_cfg_scale
# TODO Stable Cascade callbacks are currently broken in combined pipeline so preview will not get triggered
if 'prior_callback_on_step_end' in possible:
possible.remove('callback_on_step_end')
if 'callback_on_step_end' in possible:
possible.remove('callback_on_step_end')
if 'callback' in possible:
possible.remove('callback')
# set callbacks
if 'callback_steps' in possible:
args['callback_steps'] = 1
if 'callback' in possible:
args['callback'] = diffusers_callback_legacy
elif 'callback_on_step_end_tensor_inputs' in possible:
if hasattr(model, "decoder") and hasattr(model, "prior_prior") and 'prior_guidance_scale' in possible:
args['prior_callback_on_step_end'] = diffusers_callback
if 'callback_on_step_end' in possible:
args['callback_on_step_end'] = diffusers_callback
if 'prompt_embeds' in possible and 'negative_prompt_embeds' in possible and hasattr(model, '_callback_tensor_inputs'):
args['callback_on_step_end_tensor_inputs'] = model._callback_tensor_inputs # pylint: disable=protected-access
else:
args['callback_on_step_end_tensor_inputs'] = ['latents']
if 'callback_on_step_end_tensor_inputs' in possible:
if 'prompt_embeds' in possible and 'negative_prompt_embeds' in possible and hasattr(model, '_callback_tensor_inputs'):
args['callback_on_step_end_tensor_inputs'] = model._callback_tensor_inputs # pylint: disable=protected-access
else:
args['callback_on_step_end_tensor_inputs'] = ['latents']
elif 'callback' in possible:
args['callback'] = diffusers_callback_legacy
# handle remaining args
for arg in kwargs:
if arg in possible: # add kwargs
args[arg] = kwargs[arg]
@@ -261,6 +274,8 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
debug(f'Diffusers unknown task args: {k}={v}')
sd_hijack_hypertile.hypertile_set(p, hr=len(getattr(p, 'init_images', [])) > 0)
# debug info
clean = args.copy()
clean.pop('callback', None)
clean.pop('callback_steps', None)
@@ -284,8 +299,6 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
txt += f' Clamp threshold={p.hdr_threshold} boundary={p.hdr_boundary}' if p.hdr_clamp else ' Clamp off'
txt += f' Maximize boundary={p.hdr_max_boundry} center={p.hdr_max_center}' if p.hdr_maximize else ' Maximize off'
shared.log.debug(txt)
# components = [{ k: getattr(v, 'device', None) } for k, v in model.components.items()]
# shared.log.debug(f'Diffuser pipeline components: {components}')
if shared.cmd_opts.profile:
t1 = time.time()
shared.log.debug(f'Profile: pipeline args: {t1-t0:.2f}')