Refactor sampler code for more advanced sampler nodes part 2.

comfy/sampler_helpers.py

@@ -0,0 +1,76 @@
+import torch
+import comfy.model_management
+import comfy.conds
+
+def prepare_mask(noise_mask, shape, device):
+    """ensures noise mask is of proper dimensions"""
+    noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear")
+    noise_mask = torch.cat([noise_mask] * shape[1], dim=1)
+    noise_mask = comfy.utils.repeat_to_batch_size(noise_mask, shape[0])
+    noise_mask = noise_mask.to(device)
+    return noise_mask
+
+def get_models_from_cond(cond, model_type):
+    models = []
+    for c in cond:
+        if model_type in c:
+            models += [c[model_type]]
+    return models
+
+def convert_cond(cond):
+    out = []
+    for c in cond:
+        temp = c[1].copy()
+        model_conds = temp.get("model_conds", {})
+        if c[0] is not None:
+            model_conds["c_crossattn"] = comfy.conds.CONDCrossAttn(c[0]) #TODO: remove
+            temp["cross_attn"] = c[0]
+        temp["model_conds"] = model_conds
+        out.append(temp)
+    return out
+
+def get_additional_models(conds, dtype):
+    """loads additional models in conditioning"""
+    cnets = []
+    gligen = []
+
+    for k in conds:
+        cnets += get_models_from_cond(conds[k], "control")
+        gligen += get_models_from_cond(conds[k], "gligen")
+
+    control_nets = set(cnets)
+
+    inference_memory = 0
+    control_models = []
+    for m in control_nets:
+        control_models += m.get_models()
+        inference_memory += m.inference_memory_requirements(dtype)
+
+    gligen = [x[1] for x in gligen]
+    models = control_models + gligen
+    return models, inference_memory
+
+def cleanup_additional_models(models):
+    """cleanup additional models that were loaded"""
+    for m in models:
+        if hasattr(m, 'cleanup'):
+            m.cleanup()
+
+
+def prepare_sampling(model, noise_shape, conds):
+    device = model.load_device
+    real_model = None
+    models, inference_memory = get_additional_models(conds, model.model_dtype())
+    comfy.model_management.load_models_gpu([model] + models, model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory)
+    real_model = model.model
+
+    return real_model, conds, models
+
+def cleanup_models(conds, models):
+    cleanup_additional_models(models)
+
+    control_cleanup = []
+    for k in conds:
+        control_cleanup += get_models_from_cond(conds[k], "control")
+
+    cleanup_additional_models(set(control_cleanup))