mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-04-14 20:42:31 +08:00
Merge branch 'Comfy-Org:master' into fix/jobs-preview-fallback-priority
This commit is contained in:
Deniz Şafak
GitHub
commit
b3765c88aa
2
.ci/windows_intel_base_files/run_intel_gpu.bat
Executable file
2
.ci/windows_intel_base_files/run_intel_gpu.bat
Executable file
@ -0,0 +1,2 @@
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.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build
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pause
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@ -182,7 +182,7 @@
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]
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},
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"widgets_values": [
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50
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0
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]
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},
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{
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@ -316,7 +316,7 @@
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"step": 1
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},
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"widgets_values": [
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30
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0
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]
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},
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{
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@ -90,7 +90,7 @@ class HeatmapHead(torch.nn.Module):
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origin_max = np.max(hm[k])
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dr = np.zeros((H + 2 * border, W + 2 * border), dtype=np.float32)
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dr[border:-border, border:-border] = hm[k].copy()
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dr = gaussian_filter(dr, sigma=2.0)
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dr = gaussian_filter(dr, sigma=2.0, truncate=2.5)
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hm[k] = dr[border:-border, border:-border].copy()
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cur_max = np.max(hm[k])
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if cur_max > 0:
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@ -558,7 +558,7 @@ class GrokVideoReferenceNode(IO.ComfyNode):
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(
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$res := $lookup(widgets, "model.resolution");
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$dur := $lookup(widgets, "model.duration");
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$refs := inputGroups["model.reference_images"];
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$refs := $lookup(inputGroups, "model.reference_images");
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$rate := $res = "720p" ? 0.07 : 0.05;
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$price := ($rate * $dur + 0.002 * $refs) * 1.43;
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{"type":"usd","usd": $price}
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@ -32,10 +32,12 @@ class RTDETR_detect(io.ComfyNode):
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def execute(cls, model, image, threshold, class_name, max_detections) -> io.NodeOutput:
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B, H, W, C = image.shape
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image_in = comfy.utils.common_upscale(image.movedim(-1, 1), 640, 640, "bilinear", crop="disabled")
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comfy.model_management.load_model_gpu(model)
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results = model.model.diffusion_model(image_in, (W, H)) # list of B dicts
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results = []
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for i in range(0, B, 32):
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batch = image[i:i + 32]
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image_in = comfy.utils.common_upscale(batch.movedim(-1, 1), 640, 640, "bilinear", crop="disabled")
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results.extend(model.model.diffusion_model(image_in, (W, H)))
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all_bbox_dicts = []
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@ -1,5 +1,6 @@
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import torch
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import comfy.utils
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import comfy.model_management
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import numpy as np
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import math
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import colorsys
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@ -410,7 +411,9 @@ class SDPoseDrawKeypoints(io.ComfyNode):
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pose_outputs.append(canvas)
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pose_outputs_np = np.stack(pose_outputs) if len(pose_outputs) > 1 else np.expand_dims(pose_outputs[0], 0)
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final_pose_output = torch.from_numpy(pose_outputs_np).float() / 255.0
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final_pose_output = torch.from_numpy(pose_outputs_np).to(
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device=comfy.model_management.intermediate_device(),
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dtype=comfy.model_management.intermediate_dtype()) / 255.0
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return io.NodeOutput(final_pose_output)
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class SDPoseKeypointExtractor(io.ComfyNode):
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@ -459,6 +462,27 @@ class SDPoseKeypointExtractor(io.ComfyNode):
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model_h = int(head.heatmap_size[0]) * 4 # e.g. 192 * 4 = 768
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model_w = int(head.heatmap_size[1]) * 4 # e.g. 256 * 4 = 1024
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def _resize_to_model(imgs):
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"""Aspect-preserving resize + zero-pad BHWC images to (model_h, model_w). Returns (resized_bhwc, scale, pad_top, pad_left)."""
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h, w = imgs.shape[-3], imgs.shape[-2]
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scale = min(model_h / h, model_w / w)
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sh, sw = int(round(h * scale)), int(round(w * scale))
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pt, pl = (model_h - sh) // 2, (model_w - sw) // 2
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chw = imgs.permute(0, 3, 1, 2).float()
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scaled = comfy.utils.common_upscale(chw, sw, sh, upscale_method="bilinear", crop="disabled")
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padded = torch.zeros(scaled.shape[0], scaled.shape[1], model_h, model_w, dtype=scaled.dtype, device=scaled.device)
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padded[:, :, pt:pt + sh, pl:pl + sw] = scaled
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return padded.permute(0, 2, 3, 1), scale, pt, pl
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def _remap_keypoints(kp, scale, pad_top, pad_left, offset_x=0, offset_y=0):
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"""Remap keypoints from model space back to original image space."""
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kp = kp.copy() if isinstance(kp, np.ndarray) else np.array(kp, dtype=np.float32)
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invalid = kp[..., 0] < 0
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kp[..., 0] = (kp[..., 0] - pad_left) / scale + offset_x
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kp[..., 1] = (kp[..., 1] - pad_top) / scale + offset_y
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kp[invalid] = -1
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return kp
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def _run_on_latent(latent_batch):
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"""Run one forward pass and return (keypoints_list, scores_list) for the batch."""
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nonlocal captured_feat
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@ -504,36 +528,19 @@ class SDPoseKeypointExtractor(io.ComfyNode):
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if x2 <= x1 or y2 <= y1:
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continue
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crop_h_px, crop_w_px = y2 - y1, x2 - x1
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crop = img[:, y1:y2, x1:x2, :] # (1, crop_h, crop_w, C)
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# scale to fit inside (model_h, model_w) while preserving aspect ratio, then pad to exact model size.
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scale = min(model_h / crop_h_px, model_w / crop_w_px)
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scaled_h, scaled_w = int(round(crop_h_px * scale)), int(round(crop_w_px * scale))
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pad_top, pad_left = (model_h - scaled_h) // 2, (model_w - scaled_w) // 2
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crop_chw = crop.permute(0, 3, 1, 2).float() # BHWC → BCHW
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scaled = comfy.utils.common_upscale(crop_chw, scaled_w, scaled_h, upscale_method="bilinear", crop="disabled")
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padded = torch.zeros(1, scaled.shape[1], model_h, model_w, dtype=scaled.dtype, device=scaled.device)
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padded[:, :, pad_top:pad_top + scaled_h, pad_left:pad_left + scaled_w] = scaled
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crop_resized = padded.permute(0, 2, 3, 1) # BCHW → BHWC
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crop_resized, scale, pad_top, pad_left = _resize_to_model(crop)
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latent_crop = vae.encode(crop_resized)
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kp_batch, sc_batch = _run_on_latent(latent_crop)
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kp, sc = kp_batch[0], sc_batch[0] # (K, 2), coords in model pixel space
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# remove padding offset, undo scale, offset to full-image coordinates.
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kp = kp.copy() if isinstance(kp, np.ndarray) else np.array(kp, dtype=np.float32)
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kp[..., 0] = (kp[..., 0] - pad_left) / scale + x1
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kp[..., 1] = (kp[..., 1] - pad_top) / scale + y1
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kp = _remap_keypoints(kp_batch[0], scale, pad_top, pad_left, x1, y1)
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img_keypoints.append(kp)
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img_scores.append(sc)
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img_scores.append(sc_batch[0])
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else:
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# No bboxes for this image – run on the full image
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latent_img = vae.encode(img)
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img_resized, scale, pad_top, pad_left = _resize_to_model(img)
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latent_img = vae.encode(img_resized)
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kp_batch, sc_batch = _run_on_latent(latent_img)
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img_keypoints.append(kp_batch[0])
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img_keypoints.append(_remap_keypoints(kp_batch[0], scale, pad_top, pad_left))
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img_scores.append(sc_batch[0])
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all_keypoints.append(img_keypoints)
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@ -541,19 +548,16 @@ class SDPoseKeypointExtractor(io.ComfyNode):
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pbar.update(1)
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else: # full-image mode, batched
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tqdm_pbar = tqdm(total=total_images, desc="Extracting keypoints")
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for batch_start in range(0, total_images, batch_size):
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batch_end = min(batch_start + batch_size, total_images)
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latent_batch = vae.encode(image[batch_start:batch_end])
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for batch_start in tqdm(range(0, total_images, batch_size), desc="Extracting keypoints"):
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batch_resized, scale, pad_top, pad_left = _resize_to_model(image[batch_start:batch_start + batch_size])
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latent_batch = vae.encode(batch_resized)
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kp_batch, sc_batch = _run_on_latent(latent_batch)
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for kp, sc in zip(kp_batch, sc_batch):
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all_keypoints.append([kp])
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all_keypoints.append([_remap_keypoints(kp, scale, pad_top, pad_left)])
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all_scores.append([sc])
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tqdm_pbar.update(1)
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pbar.update(batch_end - batch_start)
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pbar.update(len(kp_batch))
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openpose_frames = _to_openpose_frames(all_keypoints, all_scores, height, width)
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return io.NodeOutput(openpose_frames)
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@ -6,6 +6,7 @@ import comfy.utils
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import folder_paths
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from typing_extensions import override
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from comfy_api.latest import ComfyExtension, io
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import comfy.model_management
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try:
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from spandrel_extra_arches import EXTRA_REGISTRY
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@ -78,13 +79,15 @@ class ImageUpscaleWithModel(io.ComfyNode):
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tile = 512
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overlap = 32
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output_device = comfy.model_management.intermediate_device()
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oom = True
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try:
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while oom:
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try:
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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)
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pbar = comfy.utils.ProgressBar(steps)
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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)
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s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a.float()), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar, output_device=output_device)
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oom = False
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except Exception as e:
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model_management.raise_non_oom(e)
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@ -94,7 +97,7 @@ class ImageUpscaleWithModel(io.ComfyNode):
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finally:
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upscale_model.to("cpu")
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s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0)
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s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0).to(comfy.model_management.intermediate_dtype())
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return io.NodeOutput(s)
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upscale = execute # TODO: remove
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@ -1,5 +1,5 @@
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comfyui-frontend-package==1.42.8
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comfyui-workflow-templates==0.9.44
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comfyui-frontend-package==1.42.10
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comfyui-workflow-templates==0.9.45
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comfyui-embedded-docs==0.4.3
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torch
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torchsde
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