fluxdev/stable-diffusion-webui-forge

1import torch2from ldm_patched.modules.conds import CONDRegular, CONDCrossAttn3from ldm_patched.modules.samplers import sampling_function4from ldm_patched.modules import model_management5from ldm_patched.modules.ops import cleanup_cache6 7 8def cond_from_a1111_to_patched_ldm(cond):9    if isinstance(cond, torch.Tensor):10        result = dict(11            cross_attn=cond,12            model_conds=dict(13                c_crossattn=CONDCrossAttn(cond),14            )15        )16        return [result, ]17 18    cross_attn = cond['crossattn']19    pooled_output = cond['vector']20 21    result = dict(22        cross_attn=cross_attn,23        pooled_output=pooled_output,24        model_conds=dict(25            c_crossattn=CONDCrossAttn(cross_attn),26            y=CONDRegular(pooled_output)27        )28    )29 30    return [result, ]31 32 33def cond_from_a1111_to_patched_ldm_weighted(cond, weights):34    transposed = list(map(list, zip(*weights)))35    results = []36 37    for cond_pre in transposed:38        current_indices = []39        current_weight = 040        for i, w in cond_pre:41            current_indices.append(i)42            current_weight = w43 44        if hasattr(cond, 'advanced_indexing'):45            feed = cond.advanced_indexing(current_indices)46        else:47            feed = cond[current_indices]48 49        h = cond_from_a1111_to_patched_ldm(feed)50        h[0]['strength'] = current_weight51        results += h52 53    return results54 55 56def forge_sample(self, denoiser_params, cond_scale, cond_composition):57    model = self.inner_model.inner_model.forge_objects.unet.model58    control = self.inner_model.inner_model.forge_objects.unet.controlnet_linked_list59    extra_concat_condition = self.inner_model.inner_model.forge_objects.unet.extra_concat_condition60    x = denoiser_params.x61    timestep = denoiser_params.sigma62    uncond = cond_from_a1111_to_patched_ldm(denoiser_params.text_uncond)63    cond = cond_from_a1111_to_patched_ldm_weighted(denoiser_params.text_cond, cond_composition)64    model_options = self.inner_model.inner_model.forge_objects.unet.model_options65    seed = self.p.seeds[0]66 67    if extra_concat_condition is not None:68        image_cond_in = extra_concat_condition69    else:70        image_cond_in = denoiser_params.image_cond71 72    if isinstance(image_cond_in, torch.Tensor):73        if image_cond_in.shape[0] == x.shape[0] \74                and image_cond_in.shape[2] == x.shape[2] \75                and image_cond_in.shape[3] == x.shape[3]:76            for i in range(len(uncond)):77                uncond[i]['model_conds']['c_concat'] = CONDRegular(image_cond_in)78            for i in range(len(cond)):79                cond[i]['model_conds']['c_concat'] = CONDRegular(image_cond_in)80 81    if control is not None:82        for h in cond + uncond:83            h['control'] = control84 85    for modifier in model_options.get('conditioning_modifiers', []):86        model, x, timestep, uncond, cond, cond_scale, model_options, seed = modifier(model, x, timestep, uncond, cond, cond_scale, model_options, seed)87 88    denoised = sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options, seed)89    return denoised90 91 92def sampling_prepare(unet, x):93    B, C, H, W = x.shape94 95    memory_estimation_function = unet.model_options.get('memory_peak_estimation_modifier', unet.memory_required)96 97    unet_inference_memory = memory_estimation_function([B * 2, C, H, W])98    additional_inference_memory = unet.extra_preserved_memory_during_sampling99    additional_model_patchers = unet.extra_model_patchers_during_sampling100 101    if unet.controlnet_linked_list is not None:102        additional_inference_memory += unet.controlnet_linked_list.inference_memory_requirements(unet.model_dtype())103        additional_model_patchers += unet.controlnet_linked_list.get_models()104 105    model_management.load_models_gpu(106        models=[unet] + additional_model_patchers,107        memory_required=unet_inference_memory + additional_inference_memory)108 109    real_model = unet.model110 111    percent_to_timestep_function = lambda p: real_model.model_sampling.percent_to_sigma(p)112 113    for cnet in unet.list_controlnets():114        cnet.pre_run(real_model, percent_to_timestep_function)115 116    return117 118 119def sampling_cleanup(unet):120    for cnet in unet.list_controlnets():121        cnet.cleanup()122    cleanup_cache()123    return124