Fix tests, improve distributed worker health check, add torch compile options

comfy/cmd/extra_model_paths.py

@@ -1,25 +1,4 @@
-import os
-import yaml
-import logging
-
 def load_extra_path_config(yaml_path):
-    from . import folder_paths
+    from ..extra_config import load_extra_path_config
 
-    with open(yaml_path, 'r') as stream:
+    return load_extra_path_config(yaml_path)
-        config = yaml.safe_load(stream)
-    for c in config:
-        conf = config[c]
-        if conf is None:
-            continue
-        base_path = None
-        if "base_path" in conf:
-            base_path = conf.pop("base_path")
-        for x in conf:
-            for y in conf[x].split("\n"):
-                if len(y) == 0:
-                    continue
-                full_path = y
-                if base_path is not None:
-                    full_path = os.path.join(base_path, full_path)
-                logging.info(f"Adding extra search path {x} ({full_path})")
-                folder_paths.add_model_folder_path(x, full_path)

comfy/cmd/folder_paths.py

@@ -349,7 +349,7 @@ def invalidate_cache(folder_name):
     _filename_list_cache.pop(folder_name, None)
 
 
-def filter_files_content_types(files: list[str], content_types: Literal["image", "video", "audio"]) -> list[str]:
+def filter_files_content_types(files: list[str], content_types: list[Literal["image", "video", "audio"]]) -> list[str]:
     """
     Example:
         files = os.listdir(folder_paths.get_input_directory())

comfy/distributed/distributed_prompt_worker.py

@@ -41,7 +41,14 @@ class DistributedPromptWorker:
         self._health_check_site: Optional[web.TCPSite] = None
 
     async def _health_check(self, request):
-        return web.Response(text="OK", content_type="text/plain")
+        if self._connection is None:
+            return web.Response(text="UNHEALTHY: RabbitMQ connection is not established", status=503)
+
+        is_healthy = await self._is_connection_healthy()
+        if is_healthy:
+            return web.Response(text="HEALTHY", status=200)
+        else:
+            return web.Response(text="UNHEALTHY: RabbitMQ connection is not healthy", status=503)
 
     async def _start_health_check_server(self):
         app = web.Application()
@@ -85,9 +92,27 @@ class DistributedPromptWorker:
         await self.on_did_complete_work_item(request_obj, reply)
         return asdict(reply)
 
+    async def _is_connection_healthy(self):
+        if self._connection is None:
+            return False
+
+        return (
+                not self._connection.is_closed
+                and self._connection.connected.is_set()
+                and await self._check_connection_ready()
+        )
+
+    async def _check_connection_ready(self):
+        try:
+            await asyncio.wait_for(self._connection.ready(), timeout=1.0)
+            return True
+        except asyncio.TimeoutError:
+            return False
+
     @tracer.start_as_current_span("Initialize Prompt Worker")
     async def init(self):
         await self._exit_stack.__aenter__()
+        await self._start_health_check_server()
         try:
             self._connection = await connect_robust(self._connection_uri, loop=self._loop)
         except AMQPConnectionError as connection_error:
@@ -102,7 +127,6 @@ class DistributedPromptWorker:
             await self._exit_stack.enter_async_context(self._embedded_comfy_client)
 
         await self._rpc.register(self._queue_name, self._do_work_item)
-        await self._start_health_check_server()
 
     async def __aenter__(self) -> "DistributedPromptWorker":
         await self.init()

comfy/extra_config.py

@@ -1,9 +1,12 @@
-import os
-import yaml
-import folder_paths
 import logging
+import os
+
+import yaml
+
 
 def load_extra_path_config(yaml_path):
+    from .cmd import folder_paths
+
     with open(yaml_path, 'r') as stream:
         config = yaml.safe_load(stream)
     for c in config:

comfy/k_diffusion/sampling.py

@@ -1,16 +1,17 @@
 import math
 
-from scipy import integrate
 import torch
-from torch import nn
 import torchsde
+from scipy import integrate
+from torch import nn
 from tqdm.auto import trange, tqdm
 
-from . import utils
 from . import deis
+from . import utils
 from .. import model_patcher
 from .. import model_sampling
 
+
 def append_zero(x):
     return torch.cat([x, x.new_zeros([1])])
 
@@ -274,6 +275,7 @@ def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, dis
 def linear_multistep_coeff(order, t, i, j):
     if order - 1 > i:
         raise ValueError(f'Order {order} too high for step {i}')
+
     def fn(tau):
         prod = 1.
         for k in range(order):
@@ -281,6 +283,7 @@ def linear_multistep_coeff(order, t, i, j):
                 continue
             prod *= (tau - t[i - k]) / (t[i - j] - t[i - k])
         return prod
+
     return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0]
 
 
@@ -306,6 +309,7 @@ def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, o
 
 class PIDStepSizeController:
     """A PID controller for ODE adaptive step size control."""
+
     def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8):
         self.h = h
         self.b1 = (pcoeff + icoeff + dcoeff) / order
@@ -552,17 +556,17 @@ def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, callback=Non
     noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
     sigma_fn = lambda lbda: (lbda.exp() + 1) ** -1
-    lambda_fn = lambda sigma: ((1-sigma)/sigma).log()
+    lambda_fn = lambda sigma: ((1 - sigma) / sigma).log()
 
     # logged_x = x.unsqueeze(0)
 
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
-        downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta
+        downstep_ratio = 1 + (sigmas[i + 1] / sigmas[i] - 1) * eta
-        sigma_down = sigmas[i+1] * downstep_ratio
+        sigma_down = sigmas[i + 1] * downstep_ratio
-        alpha_ip1 = 1 - sigmas[i+1]
+        alpha_ip1 = 1 - sigmas[i + 1]
         alpha_down = 1 - sigma_down
-        renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5
+        renoise_coeff = (sigmas[i + 1] ** 2 - sigma_down ** 2 * alpha_ip1 ** 2 / alpha_down ** 2) ** 0.5
         # sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
@@ -590,10 +594,11 @@ def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, callback=Non
             # print("sigma_i", sigmas[i], "sigma_ip1", sigmas[i+1],"sigma_down", sigma_down, "sigma_down_i_ratio", sigma_down_i_ratio, "sigma_s_i_ratio", sigma_s_i_ratio, "renoise_coeff", renoise_coeff)
         # Noise addition
         if sigmas[i + 1] > 0 and eta > 0:
-            x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff
+            x = (alpha_ip1 / alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff
         # logged_x = torch.cat((logged_x, x.unsqueeze(0)), dim=0)
     return x
 
+
 @torch.no_grad()
 def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
     """DPM-Solver++ (stochastic)."""
@@ -665,6 +670,7 @@ def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=No
         old_denoised = denoised
     return x
 
+
 @torch.no_grad()
 def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
     """DPM-Solver++(2M) SDE."""
@@ -713,6 +719,7 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
         h_last = h if h is not None else h_last
     return x
 
+
 @torch.no_grad()
 def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
     """DPM-Solver++(3M) SDE."""
@@ -766,6 +773,7 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
         h_1, h_2 = h, h_1
     return x
 
+
 @torch.no_grad()
 def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
     if len(sigmas) <= 1:
@@ -775,6 +783,7 @@ def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, di
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
     return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler)
 
+
 @torch.no_grad()
 def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
     if len(sigmas) <= 1:
@@ -784,6 +793,7 @@ def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, di
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
     return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
 
+
 @torch.no_grad()
 def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
     if len(sigmas) <= 1:
@@ -804,6 +814,7 @@ def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler):
         mu += ((1 - alpha) * (1. - alpha_cumprod_prev) / (1. - alpha_cumprod)).sqrt() * noise_sampler(sigma, sigma_prev)
     return mu
 
+
 def generic_step_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, step_function=None):
     extra_args = {} if extra_args is None else extra_args
     noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
@@ -823,6 +834,7 @@ def generic_step_sampler(model, x, sigmas, extra_args=None, callback=None, disab
 def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
     return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step)
 
+
 @torch.no_grad()
 def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
     extra_args = {} if extra_args is None else extra_args
@@ -839,7 +851,6 @@ def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, n
     return x
 
 
-
 @torch.no_grad()
 def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
     """
@@ -893,12 +904,11 @@ def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=Non
             d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
 
             w = 2 * sigmas[0]
-            w2 = sigmas[i+1]/w
+            w2 = sigmas[i + 1] / w
             w1 = 1 - w2
 
             d_prime = d * w1 + d_2 * w2
 
-
             x = x + d_prime * dt
 
         else:
@@ -922,8 +932,8 @@ def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=Non
     return x
 
 
-#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
+# From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
-#under Apache 2 license
+# under Apache 2 license
 def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
@@ -943,27 +953,28 @@ def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None,
 
         d_cur = (x_cur - denoised) / t_cur
 
-        order = min(max_order, i+1)
+        order = min(max_order, i + 1)
-        if order == 1:      # First Euler step.
+        if order == 1:  # First Euler step.
             x_next = x_cur + (t_next - t_cur) * d_cur
-        elif order == 2:    # Use one history point.
+        elif order == 2:  # Use one history point.
             x_next = x_cur + (t_next - t_cur) * (3 * d_cur - buffer_model[-1]) / 2
-        elif order == 3:    # Use two history points.
+        elif order == 3:  # Use two history points.
             x_next = x_cur + (t_next - t_cur) * (23 * d_cur - 16 * buffer_model[-1] + 5 * buffer_model[-2]) / 12
-        elif order == 4:    # Use three history points.
+        elif order == 4:  # Use three history points.
             x_next = x_cur + (t_next - t_cur) * (55 * d_cur - 59 * buffer_model[-1] + 37 * buffer_model[-2] - 9 * buffer_model[-3]) / 24
 
         if len(buffer_model) == max_order - 1:
             for k in range(max_order - 2):
-                buffer_model[k] = buffer_model[k+1]
+                buffer_model[k] = buffer_model[k + 1]
             buffer_model[-1] = d_cur
         else:
             buffer_model.append(d_cur)
 
     return x_next
 
-#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
+
-#under Apache 2 license
+# From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
+# under Apache 2 license
 def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
@@ -984,32 +995,32 @@ def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=Non
 
         d_cur = (x_cur - denoised) / t_cur
 
-        order = min(max_order, i+1)
+        order = min(max_order, i + 1)
-        if order == 1:      # First Euler step.
+        if order == 1:  # First Euler step.
             x_next = x_cur + (t_next - t_cur) * d_cur
-        elif order == 2:    # Use one history point.
+        elif order == 2:  # Use one history point.
             h_n = (t_next - t_cur)
-            h_n_1 = (t_cur - t_steps[i-1])
+            h_n_1 = (t_cur - t_steps[i - 1])
             coeff1 = (2 + (h_n / h_n_1)) / 2
             coeff2 = -(h_n / h_n_1) / 2
             x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1])
-        elif order == 3:    # Use two history points.
+        elif order == 3:  # Use two history points.
             h_n = (t_next - t_cur)
-            h_n_1 = (t_cur - t_steps[i-1])
+            h_n_1 = (t_cur - t_steps[i - 1])
-            h_n_2 = (t_steps[i-1] - t_steps[i-2])
+            h_n_2 = (t_steps[i - 1] - t_steps[i - 2])
             temp = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2
             coeff1 = (2 + (h_n / h_n_1)) / 2 + temp
             coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp
             coeff3 = temp * h_n_1 / h_n_2
             x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2])
-        elif order == 4:    # Use three history points.
+        elif order == 4:  # Use three history points.
             h_n = (t_next - t_cur)
-            h_n_1 = (t_cur - t_steps[i-1])
+            h_n_1 = (t_cur - t_steps[i - 1])
-            h_n_2 = (t_steps[i-1] - t_steps[i-2])
+            h_n_2 = (t_steps[i - 1] - t_steps[i - 2])
-            h_n_3 = (t_steps[i-2] - t_steps[i-3])
+            h_n_3 = (t_steps[i - 2] - t_steps[i - 3])
             temp1 = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2
             temp2 = ((1 - h_n / (3 * (h_n + h_n_1))) / 2 + (1 - h_n / (2 * (h_n + h_n_1))) * h_n / (6 * (h_n + h_n_1 + h_n_2))) \
-                   * (h_n * (h_n + h_n_1) * (h_n + h_n_1 + h_n_2)) / (h_n_1 * (h_n_1 + h_n_2) * (h_n_1 + h_n_2 + h_n_3))
+                    * (h_n * (h_n + h_n_1) * (h_n + h_n_1 + h_n_2)) / (h_n_1 * (h_n_1 + h_n_2) * (h_n_1 + h_n_2 + h_n_3))
             coeff1 = (2 + (h_n / h_n_1)) / 2 + temp1 + temp2
             coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp1 - (1 + (h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3)))) * temp2
             coeff3 = temp1 * h_n_1 / h_n_2 + ((h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * (1 + h_n_2 / h_n_3)) * temp2
@@ -1018,15 +1029,16 @@ def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=Non
 
         if len(buffer_model) == max_order - 1:
             for k in range(max_order - 2):
-                buffer_model[k] = buffer_model[k+1]
+                buffer_model[k] = buffer_model[k + 1]
             buffer_model[-1] = d_cur.detach()
         else:
             buffer_model.append(d_cur.detach())
 
     return x_next
 
-#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
+
-#under Apache 2 license
+# From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
+# under Apache 2 license
 @torch.no_grad()
 def sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=3, deis_mode='tab'):
     extra_args = {} if extra_args is None else extra_args
@@ -1050,36 +1062,38 @@ def sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None,
 
         d_cur = (x_cur - denoised) / t_cur
 
-        order = min(max_order, i+1)
+        order = min(max_order, i + 1)
         if t_next <= 0:
             order = 1
 
-        if order == 1:          # First Euler step.
+        if order == 1:  # First Euler step.
             x_next = x_cur + (t_next - t_cur) * d_cur
-        elif order == 2:        # Use one history point.
+        elif order == 2:  # Use one history point.
             coeff_cur, coeff_prev1 = coeff_list[i]
             x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1]
-        elif order == 3:        # Use two history points.
+        elif order == 3:  # Use two history points.
             coeff_cur, coeff_prev1, coeff_prev2 = coeff_list[i]
             x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2]
-        elif order == 4:        # Use three history points.
+        elif order == 4:  # Use three history points.
             coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3 = coeff_list[i]
             x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2] + coeff_prev3 * buffer_model[-3]
 
         if len(buffer_model) == max_order - 1:
             for k in range(max_order - 2):
-                buffer_model[k] = buffer_model[k+1]
+                buffer_model[k] = buffer_model[k + 1]
             buffer_model[-1] = d_cur.detach()
         else:
             buffer_model.append(d_cur.detach())
 
     return x_next
 
+
 @torch.no_grad()
 def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
     extra_args = {} if extra_args is None else extra_args
 
     temp = [0]
+
     def post_cfg_function(args):
         temp[0] = args["uncond_denoised"]
         return args["denoised"]
@@ -1099,6 +1113,7 @@ def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disabl
         x = denoised + d * sigmas[i + 1]
     return x
 
+
 @torch.no_grad()
 def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
     """Ancestral sampling with Euler method steps."""
@@ -1106,6 +1121,7 @@ def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=No
     noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
 
     temp = [0]
+
     def post_cfg_function(args):
         temp[0] = args["uncond_denoised"]
         return args["denoised"]
@@ -1126,6 +1142,8 @@ def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=No
         if sigmas[i + 1] > 0:
             x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
     return x
+
+
 @torch.no_grad()
 def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
     """Ancestral sampling with DPM-Solver++(2S) second-order steps."""
@@ -1133,12 +1151,13 @@ def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback
     noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
 
     temp = [0]
+
     def post_cfg_function(args):
         temp[0] = args["uncond_denoised"]
         return args["denoised"]
 
     model_options = extra_args.get("model_options", {}).copy()
-    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+    extra_args["model_options"] = model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
 
     s_in = x.new_ones([x.shape[0]])
     sigma_fn = lambda t: t.neg().exp()

comfy_extras/nodes/nodes_torch_compile.py

@@ -0,0 +1,52 @@
+import logging
+
+import torch
+
+from comfy.model_patcher import ModelPatcher
+
+DIFFUSION_MODEL = "diffusion_model"
+
+
+class TorchCompileModel:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+            },
+            "optional": {
+                "object_patch": ("STRING", {"default": DIFFUSION_MODEL}),
+                "fullgraph": ("BOOLEAN", {"default": False}),
+                "dynamic": ("BOOLEAN", {"default": False}),
+                "backend": ("STRING", {"default": "inductor"}),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "_for_testing"
+    EXPERIMENTAL = True
+
+    def patch(self, model: ModelPatcher, object_patch: str | None = DIFFUSION_MODEL, fullgraph: bool = False, dynamic: bool = False, backend: str = "inductor"):
+        if object_patch is None:
+            object_patch = DIFFUSION_MODEL
+        compile_kwargs = {
+            "fullgraph": fullgraph,
+            "dynamic": dynamic,
+            "backend": backend
+        }
+        if isinstance(model, ModelPatcher):
+            m = model.clone()
+            m.add_object_patch(object_patch, torch.compile(model=m.get_model_object(object_patch), **compile_kwargs))
+            return (m,)
+        elif isinstance(model, torch.nn.Module):
+            return torch.compile(model=model, **compile_kwargs),
+        else:
+            logging.warning("Encountered a model that cannot be compiled")
+            return model,
+
+
+NODE_CLASS_MAPPINGS = {
+    "TorchCompileModel": TorchCompileModel,
+}

comfy_extras/nodes_torch_compile.py

@@ -1,21 +0,0 @@
-import torch
-
-class TorchCompileModel:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model": ("MODEL",),
-                              }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
-
-    CATEGORY = "_for_testing"
-    EXPERIMENTAL = True
-
-    def patch(self, model):
-        m = model.clone()
-        m.add_object_patch("diffusion_model", torch.compile(model=m.get_model_object("diffusion_model")))
-        return (m, )
-
-NODE_CLASS_MAPPINGS = {
-    "TorchCompileModel": TorchCompileModel,
-}

tests/distributed/test_distributed_queue.py

@@ -118,7 +118,7 @@ async def check_health(url: str, max_retries: int = 5, retry_delay: float = 1.0)
         for _ in range(max_retries):
             try:
                 async with session.get(url, timeout=1) as response:
-                    if response.status == 200 and await response.text() == "OK":
+                    if response.status == 200:
                         return True
             except Exception as exc_info:
                 pass

tests/inference/test_execution.py

@@ -59,7 +59,7 @@ class _ProgressHandler(ServerStub):
         self.tuples.append((event, data, sid))
 
 
-class Client:
+class ComfyClient:
     def __init__(self, embedded_client: EmbeddedComfyClient, progress_handler: _ProgressHandler):
         self.embedded_client = embedded_client
         self.progress_handler = progress_handler
@@ -105,7 +105,7 @@ class TestExecution:
         (0,),
         (100,),
     ])
-    async def client(self, request) -> Client:
+    async def client(self, request) -> ComfyClient:
         from comfy.cmd.execution import nodes
         from .testing_pack import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
 
@@ -115,13 +115,13 @@ class TestExecution:
         configuration.cache_lru = lru_size
         progress_handler = _ProgressHandler()
         async with EmbeddedComfyClient(configuration, progress_handler=progress_handler) as embedded_client:
-            yield Client(embedded_client, progress_handler)
+            yield ComfyClient(embedded_client, progress_handler)
 
     @fixture
     def builder(self, request):
         yield GraphBuilder(prefix=request.node.name)
 
-    async def test_lazy_input(self, client: Client, builder: GraphBuilder):
+    async def test_lazy_input(self, client: ComfyClient, builder: GraphBuilder):
         g = builder
         input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
         input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
@@ -138,7 +138,7 @@ class TestExecution:
         assert result.did_run(mask)
         assert result.did_run(lazy_mix)
 
-    async def test_full_cache(self, client: Client, builder: GraphBuilder):
+    async def test_full_cache(self, client: ComfyClient, builder: GraphBuilder):
         g = builder
         input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
         input2 = g.node("StubImage", content="NOISE", height=512, width=512, batch_size=1)
@@ -152,7 +152,7 @@ class TestExecution:
         for node_id, node in g.nodes.items():
             assert not result2.did_run(node), f"Node {node_id} ran, but should have been cached"
 
-    async def test_partial_cache(self, client: Client, builder: GraphBuilder):
+    async def test_partial_cache(self, client: ComfyClient, builder: GraphBuilder):
         g = builder
         input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
         input2 = g.node("StubImage", content="NOISE", height=512, width=512, batch_size=1)
@@ -167,7 +167,7 @@ class TestExecution:
         assert not result2.did_run(input1), "Input1 should have been cached"
         assert not result2.did_run(input2), "Input2 should have been cached"
 
-    async def test_error(self, client: Client, builder: GraphBuilder):
+    async def test_error(self, client: ComfyClient, builder: GraphBuilder):
         g = builder
         input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
         # Different size of the two images
@@ -188,7 +188,7 @@ class TestExecution:
         ("foo", True),
         (5.0, False),
     ])
-    async def test_validation_error_literal(self, test_value, expect_error, client: Client, builder: GraphBuilder):
+    async def test_validation_error_literal(self, test_value, expect_error, client: ComfyClient, builder: GraphBuilder):
         g = builder
         validation1 = g.node("TestCustomValidation1", input1=test_value, input2=3.0)
         g.node("SaveImage", images=validation1.out(0))
@@ -203,7 +203,7 @@ class TestExecution:
         ("StubInt", 5),
         ("StubFloat", 5.0)
     ])
-    async def test_validation_error_edge1(self, test_type, test_value, client: Client, builder: GraphBuilder):
+    async def test_validation_error_edge1(self, test_type, test_value, client: ComfyClient, builder: GraphBuilder):
         g = builder
         stub = g.node(test_type, value=test_value)
         validation1 = g.node("TestCustomValidation1", input1=stub.out(0), input2=3.0)
@@ -216,7 +216,7 @@ class TestExecution:
         ("StubInt", 5, True),
         ("StubFloat", 5.0, False)
     ])
-    async def test_validation_error_edge2(self, test_type, test_value, expect_error, client: Client, builder: GraphBuilder):
+    async def test_validation_error_edge2(self, test_type, test_value, expect_error, client: ComfyClient, builder: GraphBuilder):
         g = builder
         stub = g.node(test_type, value=test_value)
         validation2 = g.node("TestCustomValidation2", input1=stub.out(0), input2=3.0)
@@ -232,7 +232,7 @@ class TestExecution:
         ("StubInt", 5, True),
         ("StubFloat", 5.0, False)
     ])
-    async def test_validation_error_edge3(self, test_type, test_value, expect_error, client: Client, builder: GraphBuilder):
+    async def test_validation_error_edge3(self, test_type, test_value, expect_error, client: ComfyClient, builder: GraphBuilder):
         g = builder
         stub = g.node(test_type, value=test_value)
         validation3 = g.node("TestCustomValidation3", input1=stub.out(0), input2=3.0)
@@ -248,7 +248,7 @@ class TestExecution:
         ("StubInt", 5, True),
         ("StubFloat", 5.0, False)
     ])
-    async def test_validation_error_edge4(self, test_type, test_value, expect_error, client: Client, builder: GraphBuilder):
+    async def test_validation_error_edge4(self, test_type, test_value, expect_error, client: ComfyClient, builder: GraphBuilder):
         g = builder
         stub = g.node(test_type, value=test_value)
         validation4 = g.node("TestCustomValidation4", input1=stub.out(0), input2=3.0)
@@ -260,7 +260,7 @@ class TestExecution:
         else:
             await client.run(g)
 
-    async def test_cycle_error(self, client: Client, builder: GraphBuilder):
+    async def test_cycle_error(self, client: ComfyClient, builder: GraphBuilder):
         g = builder
         input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
         input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
@@ -274,7 +274,7 @@ class TestExecution:
         with pytest.raises(ValueError):
             await client.run(g)
 
-    async def test_dynamic_cycle_error(self, client: Client, builder: GraphBuilder):
+    async def test_dynamic_cycle_error(self, client: ComfyClient, builder: GraphBuilder):
         g = builder
         input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
         input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
@@ -289,7 +289,7 @@ class TestExecution:
             assert 'prompt_id' in e.args[0], f"Did not get back a proper error message: {e}"
             assert e.args[0]['node_id'] == generator.id, "Error should have been on the generator node"
 
-    async def test_custom_is_changed(self, client: Client, builder: GraphBuilder):
+    async def test_custom_is_changed(self, client: ComfyClient, builder: GraphBuilder):
         g = builder
         # Creating the nodes in this specific order previously caused a bug
         save = g.node("SaveImage")
@@ -309,7 +309,7 @@ class TestExecution:
         assert result3.did_run(is_changed), "is_changed should have been re-run"
         assert result4.did_run(is_changed), "is_changed should not have been cached"
 
-    async def test_undeclared_inputs(self, client: Client, builder: GraphBuilder):
+    async def test_undeclared_inputs(self, client: ComfyClient, builder: GraphBuilder):
         g = builder
         input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
         input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
@@ -323,7 +323,7 @@ class TestExecution:
         expected = 255 // 4
         assert numpy.array(result_image).min() == expected and numpy.array(result_image).max() == expected, "Image should be grey"
 
-    async def test_for_loop(self, client: Client, builder: GraphBuilder):
+    async def test_for_loop(self, client: ComfyClient, builder: GraphBuilder):
         g = builder
         iterations = 4
         input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
@@ -342,7 +342,7 @@ class TestExecution:
             assert numpy.array(result_image).min() == expected and numpy.array(result_image).max() == expected, "Image should be grey"
             assert result.did_run(is_changed)
 
-    async def test_mixed_expansion_returns(self, client: Client, builder: GraphBuilder):
+    async def test_mixed_expansion_returns(self, client: ComfyClient, builder: GraphBuilder):
         g = builder
         val_list = g.node("TestMakeListNode", value1=0.1, value2=0.2, value3=0.3)
         mixed = g.node("TestMixedExpansionReturns", input1=val_list.out(0))
@@ -361,7 +361,7 @@ class TestExecution:
         for i in range(3):
             assert numpy.array(images_literal[i]).min() == 255 and numpy.array(images_literal[i]).max() == 255, "All images should be white"
 
-    async def test_mixed_lazy_results(self, client: Client, builder: GraphBuilder):
+    async def test_mixed_lazy_results(self, client: ComfyClient, builder: GraphBuilder):
         g = builder
         val_list = g.node("TestMakeListNode", value1=0.0, value2=0.5, value3=1.0)
         mask = g.node("StubMask", value=val_list.out(0), height=512, width=512, batch_size=1)
@@ -378,7 +378,7 @@ class TestExecution:
         assert numpy.array(images[1]).min() == 127 and numpy.array(images[1]).max() == 127, "Second image should be 0.5"
         assert numpy.array(images[2]).min() == 255 and numpy.array(images[2]).max() == 255, "Third image should be 1.0"
 
-    async def test_missing_node_error(self, client: Client, builder: GraphBuilder):
+    async def test_missing_node_error(self, client: ComfyClient, builder: GraphBuilder):
         g = builder
         input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
         input2 = g.node("StubImage", id="removeme", content="WHITE", height=512, width=512, batch_size=1)
@@ -397,7 +397,7 @@ class TestExecution:
         input2 = g.node("StubImage", id="removeme", content="WHITE", height=512, width=512, batch_size=1)
         await client.run(g)
 
-    async def test_output_reuse(self, client: Client, builder: GraphBuilder):
+    async def test_output_reuse(self, client: ComfyClient, builder: GraphBuilder):
         g = builder
         input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
 
@@ -410,9 +410,8 @@ class TestExecution:
         assert len(images1) == 1, "Should have 1 image"
         assert len(images2) == 1, "Should have 1 image"
 
-
     # This tests that only constant outputs are used in the call to `IS_CHANGED`
-    async def test_is_changed_with_outputs(self, client: Client, builder: GraphBuilder):
+    async def test_is_changed_with_outputs(self, client: ComfyClient, builder: GraphBuilder):
         g = builder
         input1 = g.node("StubConstantImage", value=0.5, height=512, width=512, batch_size=1)
         test_node = g.node("TestIsChangedWithConstants", image=input1.out(0), value=0.5)
@@ -433,7 +432,7 @@ class TestExecution:
     # This tests that nodes with OUTPUT_IS_LIST function correctly when they receive an ExecutionBlocker
     # as input. We also test that when that list (containing an ExecutionBlocker) is passed to a node,
     # only that one entry in the list is blocked.
-    def test_execution_block_list_output(self, client: ComfyClient, builder: GraphBuilder):
+    async def test_execution_block_list_output(self, client: ComfyClient, builder: GraphBuilder):
         g = builder
         image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
         image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
@@ -449,7 +448,7 @@ class TestExecution:
         list_output = g.node("TestMakeListNode", value1=blocker.out(0))
         output = g.node("PreviewImage", images=list_output.out(0))
 
-        result = client.run(g)
+        result = await client.run(g)
         assert result.did_run(output), "The execution should have run"
         images = result.get_images(output)
         assert len(images) == 2, "Should have 2 images"

tests/unit/comfy_test/folder_path_test.py

@@ -1,11 +1,13 @@
 ### 🗻 This file is created through the spirit of Mount Fuji at its peak
 # TODO(yoland): clean up this after I get back down
-import pytest
 import os
 import tempfile
 from unittest.mock import patch
 
-import folder_paths
+import pytest
+
+from comfy.cmd import folder_paths
+
 
 @pytest.fixture
 def temp_dir():
@@ -19,21 +21,25 @@ def test_get_directory_by_type():
     assert folder_paths.get_directory_by_type("output") == test_dir
     assert folder_paths.get_directory_by_type("invalid") is None
 
+
 def test_annotated_filepath():
     assert folder_paths.annotated_filepath("test.txt") == ("test.txt", None)
     assert folder_paths.annotated_filepath("test.txt [output]") == ("test.txt", folder_paths.get_output_directory())
     assert folder_paths.annotated_filepath("test.txt [input]") == ("test.txt", folder_paths.get_input_directory())
     assert folder_paths.annotated_filepath("test.txt [temp]") == ("test.txt", folder_paths.get_temp_directory())
 
+
 def test_get_annotated_filepath():
     default_dir = "/default/dir"
     assert folder_paths.get_annotated_filepath("test.txt", default_dir) == os.path.join(default_dir, "test.txt")
     assert folder_paths.get_annotated_filepath("test.txt [output]") == os.path.join(folder_paths.get_output_directory(), "test.txt")
 
+
 def test_add_model_folder_path():
     folder_paths.add_model_folder_path("test_folder", "/test/path")
     assert "/test/path" in folder_paths.get_folder_paths("test_folder")
 
+
 def test_recursive_search(temp_dir):
     os.makedirs(os.path.join(temp_dir, "subdir"))
     open(os.path.join(temp_dir, "file1.txt"), "w").close()
@@ -43,12 +49,14 @@ def test_recursive_search(temp_dir):
     assert set(files) == {"file1.txt", os.path.join("subdir", "file2.txt")}
     assert len(dirs) == 2  # temp_dir and subdir
 
+
 def test_filter_files_extensions():
     files = ["file1.txt", "file2.jpg", "file3.png", "file4.txt"]
     assert folder_paths.filter_files_extensions(files, [".txt"]) == ["file1.txt", "file4.txt"]
     assert folder_paths.filter_files_extensions(files, [".jpg", ".png"]) == ["file2.jpg", "file3.png"]
     assert folder_paths.filter_files_extensions(files, []) == files
 
+
 @patch("folder_paths.recursive_search")
 @patch("folder_paths.folder_names_and_paths")
 def test_get_filename_list(mock_folder_names_and_paths, mock_recursive_search):
@@ -56,6 +64,7 @@ def test_get_filename_list(mock_folder_names_and_paths, mock_recursive_search):
     mock_recursive_search.return_value = (["file1.txt", "file2.jpg"], {})
     assert folder_paths.get_filename_list("test_folder") == ["file1.txt"]
 
+
 def test_get_save_image_path(temp_dir):
     with patch("folder_paths.output_directory", temp_dir):
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path("test", temp_dir, 100, 100)

tests/unit/folder_paths_test/filter_by_content_types_test.py

@@ -1,7 +1,10 @@
-import pytest
 import os
 import tempfile
-from folder_paths import filter_files_content_types
+
+import pytest
+
+from comfy.cmd.folder_paths import filter_files_content_types
+
 
 @pytest.fixture(scope="module")
 def file_extensions():

tests/unit/utils/extra_config_test.py

@@ -1,10 +1,12 @@
-import pytest
-import yaml
 import os
 from unittest.mock import Mock, patch, mock_open
 
-from utils.extra_config import load_extra_path_config
+import pytest
-import folder_paths
+import yaml
+
+from comfy.cmd import folder_paths
+from comfy.extra_config import load_extra_path_config
+
 
 @pytest.fixture
 def mock_yaml_content():
@@ -15,10 +17,12 @@ def mock_yaml_content():
         }
     }
 
+
 @pytest.fixture
 def mock_expanded_home():
     return '/home/user'
 
+
 @pytest.fixture
 def yaml_config_with_appdata():
     return """
@@ -27,40 +31,47 @@ def yaml_config_with_appdata():
       checkpoints: 'models/checkpoints'
     """
 
+
 @pytest.fixture
 def mock_yaml_content_appdata(yaml_config_with_appdata):
     return yaml.safe_load(yaml_config_with_appdata)
 
+
 @pytest.fixture
 def mock_expandvars_appdata():
     mock = Mock()
     mock.side_effect = lambda path: path.replace('%APPDATA%', 'C:/Users/TestUser/AppData/Roaming')
     return mock
 
+
 @pytest.fixture
 def mock_add_model_folder_path():
     return Mock()
 
+
 @pytest.fixture
 def mock_expanduser(mock_expanded_home):
     def _expanduser(path):
         if path.startswith('~/'):
             return os.path.join(mock_expanded_home, path[2:])
         return path
+
     return _expanduser
 
+
 @pytest.fixture
 def mock_yaml_safe_load(mock_yaml_content):
     return Mock(return_value=mock_yaml_content)
 
+
 @patch('builtins.open', new_callable=mock_open, read_data="dummy file content")
 def test_load_extra_model_paths_expands_userpath(
-    mock_file,
+        mock_file,
-    monkeypatch,
+        monkeypatch,
-    mock_add_model_folder_path, 
+        mock_add_model_folder_path,
-    mock_expanduser, 
+        mock_expanduser,
-    mock_yaml_safe_load,
+        mock_yaml_safe_load,
-    mock_expanded_home
+        mock_expanded_home
 ):
     # Attach mocks used by load_extra_path_config
     monkeypatch.setattr(folder_paths, 'add_model_folder_path', mock_add_model_folder_path)
@@ -86,14 +97,15 @@ def test_load_extra_model_paths_expands_userpath(
     # Check if open was called with the correct file path
     mock_file.assert_called_once_with(dummy_yaml_file_name, 'r')
 
+
 @patch('builtins.open', new_callable=mock_open)
 def test_load_extra_model_paths_expands_appdata(
-    mock_file,
+        mock_file,
-    monkeypatch,
+        monkeypatch,
-    mock_add_model_folder_path,
+        mock_add_model_folder_path,
-    mock_expandvars_appdata,
+        mock_expandvars_appdata,
-    yaml_config_with_appdata,
+        yaml_config_with_appdata,
-    mock_yaml_content_appdata
+        mock_yaml_content_appdata
 ):
     # Set the mock_file to return yaml with appdata as a variable 
     mock_file.return_value.read.return_value = yaml_config_with_appdata