Merge branch 'comfyanonymous:master' into feature/maskpainting

comfy/extra_samplers/uni_pc.py

@@ -767,7 +767,7 @@ class UniPC:
                                 model_x = self.model_fn(x, vec_t)
                             model_prev_list[-1] = model_x
                 if callback is not None:
-                    callback(step_index, model_prev_list[-1], x)
+                    callback(step_index, model_prev_list[-1], x, steps)
         else:
             raise NotImplementedError()
         if denoise_to_zero:

comfy/samplers.py

@@ -623,7 +623,8 @@ class KSampler:
 
                 ddim_callback = None
                 if callback is not None:
-                    ddim_callback = lambda pred_x0, i: callback(i, pred_x0, None)
+                    total_steps = len(timesteps) - 1
+                    ddim_callback = lambda pred_x0, i: callback(i, pred_x0, None, total_steps)
 
                 sampler = DDIMSampler(self.model, device=self.device)
                 sampler.make_schedule_timesteps(ddim_timesteps=timesteps, verbose=False)
@@ -654,13 +655,14 @@ class KSampler:
                 noise = noise * sigmas[0]
 
                 k_callback = None
+                total_steps = len(sigmas) - 1
                 if callback is not None:
-                    k_callback = lambda x: callback(x["i"], x["denoised"], x["x"])
+                    k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps)
 
                 if latent_image is not None:
                     noise += latent_image
                 if self.sampler == "dpm_fast":
-                    samples = k_diffusion_sampling.sample_dpm_fast(self.model_k, noise, sigma_min, sigmas[0], self.steps, extra_args=extra_args, callback=k_callback, disable=disable_pbar)
+                    samples = k_diffusion_sampling.sample_dpm_fast(self.model_k, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=k_callback, disable=disable_pbar)
                 elif self.sampler == "dpm_adaptive":
                     samples = k_diffusion_sampling.sample_dpm_adaptive(self.model_k, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=k_callback, disable=disable_pbar)
                 else:

comfy/sd.py

@@ -514,11 +514,16 @@ class VAE:
         self.device = device
 
     def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
+        steps = samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)
+        steps += samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap)
+        steps += samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
+        pbar = utils.ProgressBar(steps)
+
         decode_fn = lambda a: (self.first_stage_model.decode(1. / self.scale_factor * a.to(self.device)) + 1.0)
         output = torch.clamp((
-            (utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8) +
+            (utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8, pbar = pbar) +
-            utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8) +
+            utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8, pbar = pbar) +
-             utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = 8))
+             utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = 8, pbar = pbar))
             / 3.0) / 2.0, min=0.0, max=1.0)
         return output
 
@@ -562,9 +567,15 @@ class VAE:
         model_management.unload_model()
         self.first_stage_model = self.first_stage_model.to(self.device)
         pixel_samples = pixel_samples.movedim(-1,1).to(self.device)
-        samples = utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4)
+
-        samples += utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4)
+        steps = pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
-        samples += utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4)
+        steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
+        steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
+        pbar = utils.ProgressBar(steps)
+
+        samples = utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
+        samples += utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
+        samples += utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
         samples /= 3.0
         self.first_stage_model = self.first_stage_model.cpu()
         samples = samples.cpu()

comfy/utils.py

@@ -1,4 +1,5 @@
 import torch
+import math
 
 def load_torch_file(ckpt, safe_load=False):
     if ckpt.lower().endswith(".safetensors"):
@@ -62,8 +63,11 @@ def common_upscale(samples, width, height, upscale_method, crop):
             s = samples
         return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
 
+def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
+    return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap)))
+
 @torch.inference_mode()
-def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3):
+def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, pbar = None):
     output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device="cpu")
     for b in range(samples.shape[0]):
         s = samples[b:b+1]
@@ -83,6 +87,33 @@ def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_am
                         mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1))
                 out[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += ps * mask
                 out_div[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += mask
+                if pbar is not None:
+                    pbar.update(1)
 
         output[b:b+1] = out/out_div
     return output
+
+
+PROGRESS_BAR_HOOK = None
+def set_progress_bar_global_hook(function):
+    global PROGRESS_BAR_HOOK
+    PROGRESS_BAR_HOOK = function
+
+class ProgressBar:
+    def __init__(self, total):
+        global PROGRESS_BAR_HOOK
+        self.total = total
+        self.current = 0
+        self.hook = PROGRESS_BAR_HOOK
+
+    def update_absolute(self, value, total=None):
+        if total is not None:
+            self.total = total
+        if value > self.total:
+            value = self.total
+        self.current = value
+        if self.hook is not None:
+            self.hook(self.current, self.total)
+
+    def update(self, value):
+        self.update_absolute(self.current + value)

comfy_extras/nodes_upscale_model.py

@@ -37,7 +37,12 @@ class ImageUpscaleWithModel:
         device = model_management.get_torch_device()
         upscale_model.to(device)
         in_img = image.movedim(-1,-3).to(device)
-        s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=128 + 64, tile_y=128 + 64, overlap = 8, upscale_amount=upscale_model.scale)
+
+        tile = 128 + 64
+        overlap = 8
+        steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap)
+        pbar = comfy.utils.ProgressBar(steps)
+        s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar)
         upscale_model.cpu()
         s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0)
         return (s,)

main.py

@@ -5,6 +5,7 @@ import shutil
 import threading
 
 from comfy.cli_args import args
+import comfy.utils
 
 if os.name == "nt":
     import logging
@@ -39,14 +40,9 @@ async def run(server, address='', port=8188, verbose=True, call_on_start=None):
     await asyncio.gather(server.start(address, port, verbose, call_on_start), server.publish_loop())
 
 def hijack_progress(server):
-    from tqdm.auto import tqdm
+    def hook(value, total):
-    orig_func = getattr(tqdm, "update")
+        server.send_sync("progress", { "value": value, "max": total}, server.client_id)
-    def wrapped_func(*args, **kwargs):
+    comfy.utils.set_progress_bar_global_hook(hook)
-        pbar = args[0]
-        v = orig_func(*args, **kwargs)
-        server.send_sync("progress", { "value": pbar.n, "max": pbar.total}, server.client_id)            
-        return v
-    setattr(tqdm, "update", wrapped_func)
 
 def cleanup_temp():
     temp_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp")

nodes.py

@@ -815,9 +815,13 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
     if "noise_mask" in latent:
         noise_mask = latent["noise_mask"]
 
+    pbar = comfy.utils.ProgressBar(steps)
+    def callback(step, x0, x, total_steps):
+        pbar.update_absolute(step + 1, total_steps)
+
     samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
                                   denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
-                                  force_full_denoise=force_full_denoise, noise_mask=noise_mask)
+                                  force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback)
     out = latent.copy()
     out["samples"] = samples
     return (out, )