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Merge pull request #2631 from AI-Casanova/prompt-callback

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Prompt Scheduling for Diffusers
This commit is contained in:
Vladimir Mandic
2023-12-18 11:56:04 -05:00
committed by GitHub
parent 4f35976572 0b3e1f475d
commit 5eccf17530
3 changed files with 59 additions and 36 deletions
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modules/processing_diffusers.py
+18 -16
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@@ -89,6 +89,15 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
if kwargs.get('latents', None) is None:
return kwargs
kwargs = correction_callback(p, timestep, kwargs)
if p.scheduled_prompt:
try:
i = (step + 1) % len(p.prompt_embeds)
kwargs["prompt_embeds"] = p.prompt_embeds[i][0:1].repeat(1, kwargs["prompt_embeds"].shape[0], 1).view(
kwargs["prompt_embeds"].shape[0], kwargs["prompt_embeds"].shape[1], -1)
kwargs["negative_prompt_embeds"] = p.negative_embeds[i][0:1].repeat(1, kwargs["negative_prompt_embeds"].shape[0], 1).view(
kwargs["negative_prompt_embeds"].shape[0], kwargs["negative_prompt_embeds"].shape[1], -1)
except Exception as e:
shared.log.debug(f"Callback: {e}")
shared.state.current_latent = kwargs['latents']
if shared.cmd_opts.profile and shared.profiler is not None:
shared.profiler.step()
@@ -293,36 +302,29 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
possible = signature.parameters.keys()
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]
prompt_embed = None
pooled = None
negative_embed = None
negative_pooled = None
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__:
try:
prompt_embed, pooled, negative_embed, negative_pooled = prompt_parser_diffusers.encode_prompts(model, prompts, negative_prompts, kwargs.pop("clip_skip", None))
prompt_parser_diffusers.encode_prompts(model, p, prompts, negative_prompts, kwargs.get("num_inference_steps", 1), 0, kwargs.pop("clip_skip", None))
# prompt_embed, pooled, negative_embed, negative_pooled = , , , ,
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 'prompt' in possible:
if hasattr(model, 'text_encoder') and 'prompt_embeds' in possible and prompt_embed is not None:
if type(pooled) == list:
pooled = pooled[0]
if type(negative_pooled) == list:
negative_pooled = negative_pooled[0]
args['prompt_embeds'] = prompt_embed
if hasattr(model, 'text_encoder') and 'prompt_embeds' in possible and p.prompt_embeds[0] is not None:
args['prompt_embeds'] = p.prompt_embeds[0]
if 'XL' in model.__class__.__name__:
args['pooled_prompt_embeds'] = pooled
args['pooled_prompt_embeds'] = p.positive_pooleds[0]
else:
args['prompt'] = prompts
if 'negative_prompt' in possible:
if hasattr(model, 'text_encoder') and 'negative_prompt_embeds' in possible and negative_embed is not None:
args['negative_prompt_embeds'] = negative_embed
if hasattr(model, 'text_encoder') and 'negative_prompt_embeds' in possible and p.negative_embeds[0] is not None:
args['negative_prompt_embeds'] = p.negative_embeds[0]
if 'XL' in model.__class__.__name__:
args['negative_pooled_prompt_embeds'] = negative_pooled
args['negative_pooled_prompt_embeds'] = p.negative_pooleds[0]
else:
args['negative_prompt'] = negative_prompts
if hasattr(model, 'scheduler') and hasattr(model.scheduler, 'noise_sampler_seed') and hasattr(model.scheduler, 'noise_sampler'):
@@ -342,7 +344,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
args['callback'] = diffusers_callback_legacy
elif 'callback_on_step_end_tensor_inputs' in possible:
args['callback_on_step_end'] = diffusers_callback
args['callback_on_step_end_tensor_inputs'] = ['latents']
args['callback_on_step_end_tensor_inputs'] = ['latents', 'prompt_embeds', 'negative_prompt_embeds']
for arg in kwargs:
if arg in possible: # add kwargs
args[arg] = kwargs[arg]