110 lines
4.7 KiB
Python
110 lines
4.7 KiB
Python
import torch
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import comfy.samplers
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import comfy.model_management
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from comfy.sample import prepare_sampling, cleanup_additional_models, get_additional_models
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from comfy.samplers import resolve_areas_and_cond_masks, wrap_model, calculate_start_end_timesteps, \
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create_cond_with_same_area_if_none, pre_run_control, apply_empty_x_to_equal_area, encode_adm, \
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blank_inpaint_image_like
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current_refiner = None
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refiner_switch_step = -1
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@torch.no_grad()
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@torch.inference_mode()
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def clip_separate(cond):
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c, p = cond[0]
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c = c[..., -1280:].clone()
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p = p["pooled_output"].clone()
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return [[c, {"pooled_output": p}]]
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@torch.no_grad()
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@torch.inference_mode()
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def sample_hacked(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
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global current_refiner
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positive = positive[:]
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negative = negative[:]
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resolve_areas_and_cond_masks(positive, noise.shape[2], noise.shape[3], device)
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resolve_areas_and_cond_masks(negative, noise.shape[2], noise.shape[3], device)
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model_wrap = wrap_model(model)
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calculate_start_end_timesteps(model_wrap, negative)
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calculate_start_end_timesteps(model_wrap, positive)
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#make sure each cond area has an opposite one with the same area
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for c in positive:
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create_cond_with_same_area_if_none(negative, c)
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for c in negative:
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create_cond_with_same_area_if_none(positive, c)
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# pre_run_control(model_wrap, negative + positive)
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pre_run_control(model_wrap, positive) # negative is not necessary in Fooocus, 0.5s faster.
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apply_empty_x_to_equal_area(list(filter(lambda c: c[1].get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x])
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apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x])
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if model.is_adm():
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positive = encode_adm(model, positive, noise.shape[0], noise.shape[3], noise.shape[2], device, "positive")
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negative = encode_adm(model, negative, noise.shape[0], noise.shape[3], noise.shape[2], device, "negative")
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if current_refiner is not None and current_refiner.model.is_adm():
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positive_refiner = encode_adm(current_refiner.model, clip_separate(positive), noise.shape[0], noise.shape[3], noise.shape[2], device, "positive")
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negative_refiner = encode_adm(current_refiner.model, clip_separate(negative), noise.shape[0], noise.shape[3], noise.shape[2], device, "negative")
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positive_refiner[0][1]['adm_encoded'].to(positive[0][1]['adm_encoded'])
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negative_refiner[0][1]['adm_encoded'].to(negative[0][1]['adm_encoded'])
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if latent_image is not None:
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latent_image = model.process_latent_in(latent_image)
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extra_args = {"cond": positive, "uncond": negative, "cond_scale": cfg, "model_options": model_options, "seed": seed}
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cond_concat = None
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if hasattr(model, 'concat_keys'): # inpaint
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cond_concat = []
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for ck in model.concat_keys:
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if denoise_mask is not None:
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if ck == "mask":
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cond_concat.append(denoise_mask[:,:1])
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elif ck == "masked_image":
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cond_concat.append(latent_image) #NOTE: the latent_image should be masked by the mask in pixel space
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else:
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if ck == "mask":
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cond_concat.append(torch.ones_like(noise)[:, :1])
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elif ck == "masked_image":
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cond_concat.append(blank_inpaint_image_like(noise))
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extra_args["cond_concat"] = cond_concat
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def refiner_switch():
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extra_args["cond"] = positive_refiner
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extra_args["uncond"] = negative_refiner
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# clear ip-adapter for refiner
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extra_args['model_options'] = {k: {} if k == 'transformer_options' else v for k, v in extra_args['model_options'].items()}
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models, inference_memory = get_additional_models(positive_refiner, negative_refiner, current_refiner.model_dtype())
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comfy.model_management.load_models_gpu([current_refiner] + models, comfy.model_management.batch_area_memory(
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noise.shape[0] * noise.shape[2] * noise.shape[3]) + inference_memory)
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model_wrap.inner_model.inner_model = current_refiner.model
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print('Refiner Swapped')
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return
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def callback_wrap(step, x0, x, total_steps):
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if step == refiner_switch_step and current_refiner is not None:
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refiner_switch()
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if callback is not None:
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callback(step, x0, x, total_steps)
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samples = sampler.sample(model_wrap, sigmas, extra_args, callback_wrap, noise, latent_image, denoise_mask, disable_pbar)
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return model.process_latent_out(samples.to(torch.float32))
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comfy.samplers.sample = sample_hacked
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