Merge branch 'comfyanonymous:master' into feat/is_change_object_storage

.github/workflows/test-build.yml

@@ -0,0 +1,31 @@
+name: Build package
+
+#
+# This workflow is a test of the python package build.
+# Install Python dependencies across different Python versions.
+#
+
+on:
+  push:
+    paths:
+      - "requirements.txt"
+      - ".github/workflows/test-build.yml"
+
+jobs:
+  build:
+    name: Build Test
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt

comfy/k_diffusion/sampling.py

@@ -706,3 +706,34 @@ def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disab
     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_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r)
 
+
+def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler):
+    alpha_cumprod = 1 / ((sigma * sigma) + 1)
+    alpha_cumprod_prev = 1 / ((sigma_prev * sigma_prev) + 1)
+    alpha = (alpha_cumprod / alpha_cumprod_prev)
+
+    mu = (1.0 / alpha).sqrt() * (x - (1 - alpha) * noise / (1 - alpha_cumprod).sqrt())
+    if sigma_prev > 0:
+        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
+    s_in = x.new_ones([x.shape[0]])
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        x = step_function(x / torch.sqrt(1.0 + sigmas[i] ** 2.0), sigmas[i], sigmas[i + 1], (x - denoised) / sigmas[i], noise_sampler)
+        if sigmas[i + 1] != 0:
+            x *= torch.sqrt(1.0 + sigmas[i + 1] ** 2.0)
+    return x
+
+
+@torch.no_grad()
+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)
+

comfy/latent_formats.py

@@ -1,5 +1,9 @@
 
 class LatentFormat:
+    scale_factor = 1.0
+    latent_rgb_factors = None
+    taesd_decoder_name = None
+
     def process_in(self, latent):
         return latent * self.scale_factor
 

comfy/ldm/models/diffusion/ddim.py

@@ -33,7 +33,6 @@ class DDIMSampler(object):
         assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
         to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.device)
 
-        self.register_buffer('betas', to_torch(self.model.betas))
         self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
         self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
 
@@ -195,7 +194,7 @@ class DDIMSampler(object):
                       temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
                       unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None,
                       ucg_schedule=None, denoise_function=None, extra_args=None, to_zero=True, end_step=None, disable_pbar=False):
-        device = self.model.betas.device
+        device = self.model.alphas_cumprod.device
         b = shape[0]
         if x_T is None:
             img = torch.randn(shape, device=device)

comfy/model_base.py

@@ -181,7 +181,7 @@ class SDXLRefiner(BaseModel):
         out.append(self.embedder(torch.Tensor([crop_h])))
         out.append(self.embedder(torch.Tensor([crop_w])))
         out.append(self.embedder(torch.Tensor([aesthetic_score])))
-        flat = torch.flatten(torch.cat(out))[None, ]
+        flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
         return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
 
 class SDXL(BaseModel):
@@ -206,5 +206,5 @@ class SDXL(BaseModel):
         out.append(self.embedder(torch.Tensor([crop_w])))
         out.append(self.embedder(torch.Tensor([target_height])))
         out.append(self.embedder(torch.Tensor([target_width])))
-        flat = torch.flatten(torch.cat(out))[None, ]
+        flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
         return torch.cat((clip_pooled.to(flat.device), flat), dim=1)

comfy/model_management.py

@@ -165,6 +165,9 @@ try:
                 ENABLE_PYTORCH_ATTENTION = True
             if torch.cuda.is_bf16_supported():
                 VAE_DTYPE = torch.bfloat16
+    if is_intel_xpu():
+        if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
+            ENABLE_PYTORCH_ATTENTION = True
 except:
     pass
 
@@ -478,6 +481,23 @@ def get_autocast_device(dev):
         return dev.type
     return "cuda"
 
+def cast_to_device(tensor, device, dtype, copy=False):
+    device_supports_cast = False
+    if tensor.dtype == torch.float32 or tensor.dtype == torch.float16:
+        device_supports_cast = True
+    elif tensor.dtype == torch.bfloat16:
+        if hasattr(device, 'type') and device.type.startswith("cuda"):
+            device_supports_cast = True
+
+    if device_supports_cast:
+        if copy:
+            if tensor.device == device:
+                return tensor.to(dtype, copy=copy)
+            return tensor.to(device, copy=copy).to(dtype)
+        else:
+            return tensor.to(device).to(dtype)
+    else:
+        return tensor.to(dtype).to(device, copy=copy)
 
 def xformers_enabled():
     global directml_enabled

comfy/model_patcher.py

@@ -3,6 +3,7 @@ import copy
 import inspect
 
 import comfy.utils
+import comfy.model_management
 
 class ModelPatcher:
     def __init__(self, model, load_device, offload_device, size=0, current_device=None):
@@ -154,7 +155,7 @@ class ModelPatcher:
                 self.backup[key] = weight.to(self.offload_device)
 
             if device_to is not None:
-                temp_weight = weight.float().to(device_to, copy=True)
+                temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
             else:
                 temp_weight = weight.to(torch.float32, copy=True)
             out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype)
@@ -185,15 +186,15 @@ class ModelPatcher:
                     if w1.shape != weight.shape:
                         print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
                     else:
-                        weight += alpha * w1.type(weight.dtype).to(weight.device)
+                        weight += alpha * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype)
             elif len(v) == 4: #lora/locon
-                mat1 = v[0].float().to(weight.device)
+                mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32)
-                mat2 = v[1].float().to(weight.device)
+                mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32)
                 if v[2] is not None:
                     alpha *= v[2] / mat2.shape[0]
                 if v[3] is not None:
                     #locon mid weights, hopefully the math is fine because I didn't properly test it
-                    mat3 = v[3].float().to(weight.device)
+                    mat3 = comfy.model_management.cast_to_device(v[3], weight.device, torch.float32)
                     final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]]
                     mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1)
                 try:
@@ -212,18 +213,23 @@ class ModelPatcher:
 
                 if w1 is None:
                     dim = w1_b.shape[0]
-                    w1 = torch.mm(w1_a.float(), w1_b.float())
+                    w1 = torch.mm(comfy.model_management.cast_to_device(w1_a, weight.device, torch.float32),
+                                  comfy.model_management.cast_to_device(w1_b, weight.device, torch.float32))
                 else:
-                    w1 = w1.float().to(weight.device)
+                    w1 = comfy.model_management.cast_to_device(w1, weight.device, torch.float32)
 
                 if w2 is None:
                     dim = w2_b.shape[0]
                     if t2 is None:
-                        w2 = torch.mm(w2_a.float().to(weight.device), w2_b.float().to(weight.device))
+                        w2 = torch.mm(comfy.model_management.cast_to_device(w2_a, weight.device, torch.float32),
+                                      comfy.model_management.cast_to_device(w2_b, weight.device, torch.float32))
                     else:
-                        w2 = torch.einsum('i j k l, j r, i p -> p r k l', t2.float().to(weight.device), w2_b.float().to(weight.device), w2_a.float().to(weight.device))
+                        w2 = torch.einsum('i j k l, j r, i p -> p r k l',
+                                          comfy.model_management.cast_to_device(t2, weight.device, torch.float32),
+                                          comfy.model_management.cast_to_device(w2_b, weight.device, torch.float32),
+                                          comfy.model_management.cast_to_device(w2_a, weight.device, torch.float32))
                 else:
-                    w2 = w2.float().to(weight.device)
+                    w2 = comfy.model_management.cast_to_device(w2, weight.device, torch.float32)
 
                 if len(w2.shape) == 4:
                     w1 = w1.unsqueeze(2).unsqueeze(2)
@@ -244,11 +250,20 @@ class ModelPatcher:
                 if v[5] is not None: #cp decomposition
                     t1 = v[5]
                     t2 = v[6]
-                    m1 = torch.einsum('i j k l, j r, i p -> p r k l', t1.float().to(weight.device), w1b.float().to(weight.device), w1a.float().to(weight.device))
+                    m1 = torch.einsum('i j k l, j r, i p -> p r k l',
-                    m2 = torch.einsum('i j k l, j r, i p -> p r k l', t2.float().to(weight.device), w2b.float().to(weight.device), w2a.float().to(weight.device))
+                                      comfy.model_management.cast_to_device(t1, weight.device, torch.float32),
+                                      comfy.model_management.cast_to_device(w1b, weight.device, torch.float32),
+                                      comfy.model_management.cast_to_device(w1a, weight.device, torch.float32))
+
+                    m2 = torch.einsum('i j k l, j r, i p -> p r k l',
+                                      comfy.model_management.cast_to_device(t2, weight.device, torch.float32),
+                                      comfy.model_management.cast_to_device(w2b, weight.device, torch.float32),
+                                      comfy.model_management.cast_to_device(w2a, weight.device, torch.float32))
                 else:
-                    m1 = torch.mm(w1a.float().to(weight.device), w1b.float().to(weight.device))
+                    m1 = torch.mm(comfy.model_management.cast_to_device(w1a, weight.device, torch.float32),
-                    m2 = torch.mm(w2a.float().to(weight.device), w2b.float().to(weight.device))
+                                  comfy.model_management.cast_to_device(w1b, weight.device, torch.float32))
+                    m2 = torch.mm(comfy.model_management.cast_to_device(w2a, weight.device, torch.float32),
+                                  comfy.model_management.cast_to_device(w2b, weight.device, torch.float32))
 
                 try:
                     weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype)

comfy/samplers.py

@@ -7,6 +7,7 @@ from .ldm.models.diffusion.ddim import DDIMSampler
 from .ldm.modules.diffusionmodules.util import make_ddim_timesteps
 import math
 from comfy import model_base
+import comfy.utils
 
 def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9)
     return abs(a*b) // math.gcd(a, b)
@@ -255,6 +256,7 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
                     else:
                         transformer_options["patches"] = patches
 
+                transformer_options["cond_or_uncond"] = cond_or_uncond[:]
                 c['transformer_options'] = transformer_options
 
                 if 'model_function_wrapper' in model_options:
@@ -537,7 +539,7 @@ def encode_adm(model, conds, batch_size, width, height, device, prompt_type):
 
         if adm_out is not None:
             x[1] = x[1].copy()
-            x[1]["adm_encoded"] = torch.cat([adm_out] * batch_size).to(device)
+            x[1]["adm_encoded"] = comfy.utils.repeat_to_batch_size(adm_out, batch_size).to(device)
 
     return conds
 
@@ -546,7 +548,7 @@ class KSampler:
     SCHEDULERS = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"]
     SAMPLERS = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
                 "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
-                "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddim", "uni_pc", "uni_pc_bh2"]
+                "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "ddim", "uni_pc", "uni_pc_bh2"]
 
     def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}):
         self.model = model

comfy/sd1_clip.py

@@ -71,6 +71,7 @@ class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         self.empty_tokens = [[49406] + [49407] * 76]
         self.text_projection = torch.nn.Parameter(torch.eye(self.transformer.get_input_embeddings().weight.shape[1]))
         self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
+        self.enable_attention_masks = False
 
         self.layer_norm_hidden_state = True
         if layer == "hidden":
@@ -147,7 +148,17 @@ class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder):
             precision_scope = lambda a, b: contextlib.nullcontext(a)
 
         with precision_scope(model_management.get_autocast_device(device), torch.float32):
-            outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer=="hidden")
+            attention_mask = None
+            if self.enable_attention_masks:
+                attention_mask = torch.zeros_like(tokens)
+                max_token = self.transformer.get_input_embeddings().weight.shape[0] - 1
+                for x in range(attention_mask.shape[0]):
+                    for y in range(attention_mask.shape[1]):
+                        attention_mask[x, y] = 1
+                        if tokens[x, y] == max_token:
+                            break
+
+            outputs = self.transformer(input_ids=tokens, attention_mask=attention_mask, output_hidden_states=self.layer=="hidden")
             self.transformer.set_input_embeddings(backup_embeds)
 
             if self.layer == "last":

comfy/utils.py

@@ -3,6 +3,8 @@ import math
 import struct
 import comfy.checkpoint_pickle
 import safetensors.torch
+import numpy as np
+from PIL import Image
 
 def load_torch_file(ckpt, safe_load=False, device=None):
     if device is None:
@@ -346,6 +348,13 @@ def bislerp(samples, width, height):
     result = result.reshape(n, h_new, w_new, c).movedim(-1, 1)
     return result
 
+def lanczos(samples, width, height):
+    images = [Image.fromarray(np.clip(255. * image.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples]
+    images = [image.resize((width, height), resample=Image.Resampling.LANCZOS) for image in images]
+    images = [torch.from_numpy(np.array(image).astype(np.float32) / 255.0).movedim(-1, 0) for image in images]
+    result = torch.stack(images)
+    return result
+
 def common_upscale(samples, width, height, upscale_method, crop):
         if crop == "center":
             old_width = samples.shape[3]
@@ -364,6 +373,8 @@ def common_upscale(samples, width, height, upscale_method, crop):
 
         if upscale_method == "bislerp":
             return bislerp(s, width, height)
+        elif upscale_method == "lanczos":
+            return lanczos(s, width, height)
         else:
             return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
 

comfy_extras/nodes_model_merging.py

@@ -37,7 +37,7 @@ class ModelSubtract:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "merge"
 
-    CATEGORY = "_for_testing/model_merging"
+    CATEGORY = "advanced/model_merging"
 
     def merge(self, model1, model2, multiplier):
         m = model1.clone()
@@ -55,7 +55,7 @@ class ModelAdd:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "merge"
 
-    CATEGORY = "_for_testing/model_merging"
+    CATEGORY = "advanced/model_merging"
 
     def merge(self, model1, model2):
         m = model1.clone()

comfy_extras/nodes_post_processing.py

@@ -211,7 +211,7 @@ class Sharpen:
         return (result,)
 
 class ImageScaleToTotalPixels:
-    upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic"]
+    upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
     crop_methods = ["disabled", "center"]
 
     @classmethod

custom_nodes/example_node.py.example

@@ -54,7 +54,13 @@ class Example:
                     "step": 64, #Slider's step
                     "display": "number" # Cosmetic only: display as "number" or "slider"
                 }),
-                "float_field": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "display": "number"}),
+                "float_field": ("FLOAT", {
+                    "default": 1.0,
+                    "min": 0.0,
+                    "max": 10.0,
+                    "step": 0.01,
+                    "round": 0.001, #The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
+                    "display": "number"}),
                 "print_to_screen": (["enable", "disable"],),
                 "string_field": ("STRING", {
                     "multiline": False, #True if you want the field to look like the one on the ClipTextEncode node

latent_preview.py

@@ -53,7 +53,9 @@ def get_previewer(device, latent_format):
     method = args.preview_method
     if method != LatentPreviewMethod.NoPreviews:
         # TODO previewer methods
-        taesd_decoder_path = folder_paths.get_full_path("vae_approx", latent_format.taesd_decoder_name)
+        taesd_decoder_path = None
+        if latent_format.taesd_decoder_name is not None:
+            taesd_decoder_path = folder_paths.get_full_path("vae_approx", latent_format.taesd_decoder_name)
 
         if method == LatentPreviewMethod.Auto:
             method = LatentPreviewMethod.Latent2RGB
@@ -68,7 +70,8 @@ def get_previewer(device, latent_format):
                 print("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name))
 
         if previewer is None:
-            previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors)
+            if latent_format.latent_rgb_factors is not None:
+                previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors)
     return previewer
 
 

nodes.py

@@ -543,8 +543,8 @@ class LoraLoader:
         return {"required": { "model": ("MODEL",),
                               "clip": ("CLIP", ),
                               "lora_name": (folder_paths.get_filename_list("loras"), ),
-                              "strength_model": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+                              "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
-                              "strength_clip": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+                              "strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
                               }}
     RETURN_TYPES = ("MODEL", "CLIP")
     FUNCTION = "load_lora"
@@ -889,8 +889,8 @@ class EmptyLatentImage:
 
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
+        return {"required": { "width": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
-                              "height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
+                              "height": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
                               "batch_size": ("INT", {"default": 1, "min": 1, "max": 64})}}
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "generate"
@@ -1217,7 +1217,7 @@ class KSampler:
                     {"model": ("MODEL",),
                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
+                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
                     "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
                     "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
                     "positive": ("CONDITIONING", ),
@@ -1243,7 +1243,7 @@ class KSamplerAdvanced:
                     "add_noise": (["enable", "disable"], ),
                     "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
+                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
                     "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
                     "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
                     "positive": ("CONDITIONING", ),
@@ -1423,7 +1423,7 @@ class LoadImageMask:
         return True
 
 class ImageScale:
-    upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic"]
+    upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
     crop_methods = ["disabled", "center"]
 
     @classmethod
@@ -1444,7 +1444,7 @@ class ImageScale:
         return (s,)
 
 class ImageScaleBy:
-    upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic"]
+    upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
 
     @classmethod
     def INPUT_TYPES(s):

pytest.ini

@@ -0,0 +1,5 @@
+[pytest]
+markers = 
+  inference: mark as inference test (deselect with '-m "not inference"')
+testpaths = tests
+addopts = -s

server.py

@@ -132,12 +132,12 @@ class PromptServer():
         @routes.get("/extensions")
         async def get_extensions(request):
             files = glob.glob(os.path.join(
-                self.web_root, 'extensions/**/*.js'), recursive=True)
+                glob.escape(self.web_root), 'extensions/**/*.js'), recursive=True)
             
             extensions = list(map(lambda f: "/" + os.path.relpath(f, self.web_root).replace("\\", "/"), files))
             
             for name, dir in nodes.EXTENSION_WEB_DIRS.items():
-                files = glob.glob(os.path.join(dir, '**/*.js'), recursive=True)
+                files = glob.glob(os.path.join(glob.escape(dir), '**/*.js'), recursive=True)
                 extensions.extend(list(map(lambda f: "/extensions/" + urllib.parse.quote(
                     name) + "/" + os.path.relpath(f, dir).replace("\\", "/"), files)))
 

tests/README.md

@@ -0,0 +1,29 @@
+# Automated Testing
+
+## Running tests locally
+
+Additional requirements for running tests:
+```
+pip install pytest
+pip install websocket-client==1.6.1
+opencv-python==4.6.0.66
+scikit-image==0.21.0
+```
+Run inference tests:
+```
+pytest tests/inference
+```
+
+## Quality regression test
+Compares images in 2 directories to ensure they are the same
+
+1) Run an inference test to save a directory of "ground truth" images
+```
+    pytest tests/inference --output_dir tests/inference/baseline
+```
+2) Make code edits
+
+3) Run inference and quality comparison tests
+```
+pytest
+```

tests/compare/conftest.py

@@ -0,0 +1,41 @@
+import os
+import pytest
+
+# Command line arguments for pytest
+def pytest_addoption(parser):
+    parser.addoption('--baseline_dir', action="store", default='tests/inference/baseline', help='Directory for ground-truth images')
+    parser.addoption('--test_dir', action="store", default='tests/inference/samples', help='Directory for images to test')
+    parser.addoption('--metrics_file', action="store", default='tests/metrics.md', help='Output file for metrics')
+    parser.addoption('--img_output_dir', action="store", default='tests/compare/samples', help='Output directory for diff metric images')
+
+# This initializes args at the beginning of the test session
+@pytest.fixture(scope="session", autouse=True)
+def args_pytest(pytestconfig):
+    args = {}
+    args['baseline_dir'] = pytestconfig.getoption('baseline_dir')
+    args['test_dir'] = pytestconfig.getoption('test_dir')
+    args['metrics_file'] = pytestconfig.getoption('metrics_file')
+    args['img_output_dir'] = pytestconfig.getoption('img_output_dir')
+
+    # Initialize metrics file
+    with open(args['metrics_file'], 'a') as f:
+        # if file is empty, write header
+        if os.stat(args['metrics_file']).st_size == 0:
+            f.write("| date | run | file | status | value | \n")
+            f.write("| --- | --- | --- | --- | --- | \n")
+
+    return args
+
+
+def gather_file_basenames(directory: str):
+    files = []
+    for file in os.listdir(directory):
+        if file.endswith(".png"):
+            files.append(file)
+    return files
+
+# Creates the list of baseline file names to use as a fixture
+def pytest_generate_tests(metafunc):
+    if "baseline_fname" in metafunc.fixturenames:
+        baseline_fnames = gather_file_basenames(metafunc.config.getoption("baseline_dir"))
+        metafunc.parametrize("baseline_fname", baseline_fnames)

tests/compare/test_quality.py

@@ -0,0 +1,195 @@
+import datetime
+import numpy as np
+import os
+from PIL import Image
+import pytest
+from pytest import fixture
+from typing import Tuple, List
+
+from cv2 import imread, cvtColor, COLOR_BGR2RGB
+from skimage.metrics import structural_similarity as ssim
+
+
+"""
+This test suite compares images in 2 directories by file name
+The directories are specified by the command line arguments --baseline_dir and --test_dir
+
+"""
+# ssim: Structural Similarity Index
+# Returns a tuple of (ssim, diff_image)
+def ssim_score(img0: np.ndarray, img1: np.ndarray) -> Tuple[float, np.ndarray]:
+    score, diff = ssim(img0, img1, channel_axis=-1, full=True)
+    # rescale the difference image to 0-255 range
+    diff = (diff * 255).astype("uint8")
+    return score, diff
+    
+# Metrics must return a tuple of (score, diff_image)
+METRICS = {"ssim": ssim_score}
+METRICS_PASS_THRESHOLD = {"ssim": 0.95}
+
+
+class TestCompareImageMetrics:
+    @fixture(scope="class")
+    def test_file_names(self, args_pytest):
+        test_dir = args_pytest['test_dir']
+        fnames = self.gather_file_basenames(test_dir)  
+        yield fnames
+        del fnames
+
+    @fixture(scope="class", autouse=True)
+    def teardown(self, args_pytest):
+        yield
+        # Runs after all tests are complete
+        # Aggregate output files into a grid of images
+        baseline_dir = args_pytest['baseline_dir']
+        test_dir = args_pytest['test_dir']
+        img_output_dir = args_pytest['img_output_dir']
+        metrics_file = args_pytest['metrics_file']
+
+        grid_dir = os.path.join(img_output_dir, "grid")
+        os.makedirs(grid_dir, exist_ok=True)
+
+        for metric_dir in METRICS.keys():
+            metric_path = os.path.join(img_output_dir, metric_dir)
+            for file in os.listdir(metric_path):
+                if file.endswith(".png"):
+                    score = self.lookup_score_from_fname(file, metrics_file)
+                    image_file_list = []
+                    image_file_list.append([
+                                            os.path.join(baseline_dir, file),
+                                            os.path.join(test_dir, file),
+                                            os.path.join(metric_path, file)
+                                            ])
+                    # Create grid
+                    image_list = [[Image.open(file) for file in files] for files in image_file_list]
+                    grid = self.image_grid(image_list)
+                    grid.save(os.path.join(grid_dir, f"{metric_dir}_{score:.3f}_{file}"))
+    
+    # Tests run for each baseline file name
+    @fixture()
+    def fname(self, baseline_fname):
+        yield baseline_fname
+        del baseline_fname
+    
+    def test_directories_not_empty(self, args_pytest):
+        baseline_dir = args_pytest['baseline_dir']
+        test_dir = args_pytest['test_dir']
+        assert len(os.listdir(baseline_dir)) != 0, f"Baseline directory {baseline_dir} is empty"
+        assert len(os.listdir(test_dir)) != 0, f"Test directory {test_dir} is empty"
+
+    def test_dir_has_all_matching_metadata(self, fname, test_file_names, args_pytest):
+        # Check that all files in baseline_dir have a file in test_dir with matching metadata
+        baseline_file_path = os.path.join(args_pytest['baseline_dir'], fname)
+        file_paths = [os.path.join(args_pytest['test_dir'], f) for f in test_file_names]
+        file_match = self.find_file_match(baseline_file_path, file_paths)
+        assert file_match is not None, f"Could not find a file in {args_pytest['test_dir']} with matching metadata to {baseline_file_path}"
+
+    # For a baseline image file, finds the corresponding file name in test_dir and 
+    # compares the images using the metrics in METRICS
+    @pytest.mark.parametrize("metric", METRICS.keys())
+    def test_pipeline_compare(
+        self,
+        args_pytest,
+        fname,
+        test_file_names,
+        metric,
+    ):
+        baseline_dir = args_pytest['baseline_dir']
+        test_dir = args_pytest['test_dir']
+        metrics_output_file = args_pytest['metrics_file']
+        img_output_dir = args_pytest['img_output_dir']
+        
+        baseline_file_path = os.path.join(baseline_dir, fname)
+
+        # Find file match
+        file_paths = [os.path.join(test_dir, f) for f in test_file_names]
+        test_file = self.find_file_match(baseline_file_path, file_paths)
+
+        # Run metrics
+        sample_baseline = self.read_img(baseline_file_path)
+        sample_secondary = self.read_img(test_file)
+        
+        score, metric_img = METRICS[metric](sample_baseline, sample_secondary)
+        metric_status = score > METRICS_PASS_THRESHOLD[metric]
+
+        # Save metric values
+        with open(metrics_output_file, 'a') as f:
+            run_info = os.path.splitext(fname)[0]
+            metric_status_str = "PASS ✅" if metric_status else "FAIL ❌"
+            date_str = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            f.write(f"| {date_str} | {run_info} | {metric} | {metric_status_str} | {score} | \n")
+
+        # Save metric image
+        metric_img_dir = os.path.join(img_output_dir, metric)
+        os.makedirs(metric_img_dir, exist_ok=True)
+        output_filename = f'{fname}'
+        Image.fromarray(metric_img).save(os.path.join(metric_img_dir, output_filename))
+
+        assert score > METRICS_PASS_THRESHOLD[metric]
+
+    def read_img(self, filename: str) -> np.ndarray:
+        cvImg = imread(filename)
+        cvImg = cvtColor(cvImg, COLOR_BGR2RGB)
+        return cvImg
+
+    def image_grid(self, img_list: list[list[Image.Image]]):
+        # imgs is a 2D list of images
+        # Assumes the input images are a rectangular grid of equal sized images
+        rows = len(img_list)
+        cols = len(img_list[0])
+
+        w, h = img_list[0][0].size
+        grid = Image.new('RGB', size=(cols*w, rows*h))
+        
+        for i, row in enumerate(img_list):
+            for j, img in enumerate(row):
+                grid.paste(img, box=(j*w, i*h))
+        return grid
+
+    def lookup_score_from_fname(self,
+                                fname: str,
+                                metrics_output_file: str
+        ) -> float:
+        fname_basestr = os.path.splitext(fname)[0]
+        with open(metrics_output_file, 'r') as f:
+            for line in f:
+                if fname_basestr in line:
+                    score = float(line.split('|')[5])
+                    return score
+        raise ValueError(f"Could not find score for {fname} in {metrics_output_file}")
+
+    def gather_file_basenames(self, directory: str):
+        files = []
+        for file in os.listdir(directory):
+            if file.endswith(".png"):
+                files.append(file)
+        return files
+
+    def read_file_prompt(self, fname:str) -> str:
+        # Read prompt from image file metadata
+        img = Image.open(fname)
+        img.load()
+        return img.info['prompt']
+    
+    def find_file_match(self, baseline_file: str, file_paths: List[str]):
+        # Find a file in file_paths with matching metadata to baseline_file
+        baseline_prompt = self.read_file_prompt(baseline_file)
+
+        # Do not match empty prompts
+        if baseline_prompt is None or baseline_prompt == "":
+            return None
+
+        # Find file match
+        # Reorder test_file_names so that the file with matching name is first
+        # This is an optimization because matching file names are more likely 
+        # to have matching metadata if they were generated with the same script
+        basename = os.path.basename(baseline_file)
+        file_path_basenames = [os.path.basename(f) for f in file_paths]
+        if basename in file_path_basenames:
+            match_index = file_path_basenames.index(basename)
+            file_paths.insert(0, file_paths.pop(match_index))
+
+        for f in file_paths:
+            test_file_prompt = self.read_file_prompt(f)
+            if baseline_prompt == test_file_prompt:
+                return f

tests/conftest.py

@@ -0,0 +1,36 @@
+import os
+import pytest
+
+# Command line arguments for pytest
+def pytest_addoption(parser):
+    parser.addoption('--output_dir', action="store", default='tests/inference/samples', help='Output directory for generated images')
+    parser.addoption("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0", help="Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)")
+    parser.addoption("--port", type=int, default=8188, help="Set the listen port.")
+
+# This initializes args at the beginning of the test session
+@pytest.fixture(scope="session", autouse=True)
+def args_pytest(pytestconfig):
+    args = {}
+    args['output_dir'] = pytestconfig.getoption('output_dir')
+    args['listen'] = pytestconfig.getoption('listen')
+    args['port'] = pytestconfig.getoption('port')
+
+    os.makedirs(args['output_dir'], exist_ok=True)
+
+    return args
+
+def pytest_collection_modifyitems(items):
+    # Modifies items so tests run in the correct order
+    
+    LAST_TESTS = ['test_quality']
+
+    # Move the last items to the end
+    last_items = []
+    for test_name in LAST_TESTS:
+        for item in items.copy():
+            print(item.module.__name__, item)
+            if item.module.__name__  == test_name:
+                last_items.append(item)
+                items.remove(item)
+
+    items.extend(last_items)

tests/inference/graphs/default_graph_sdxl1_0.json

@@ -0,0 +1,144 @@
+{
+    "4": {
+      "inputs": {
+        "ckpt_name": "sd_xl_base_1.0.safetensors"
+      },
+      "class_type": "CheckpointLoaderSimple"
+    },
+    "5": {
+      "inputs": {
+        "width": 1024,
+        "height": 1024,
+        "batch_size": 1
+      },
+      "class_type": "EmptyLatentImage"
+    },
+    "6": {
+      "inputs": {
+        "text": "a photo of a cat",
+        "clip": [
+          "4",
+          1
+        ]
+      },
+      "class_type": "CLIPTextEncode"
+    },
+    "10": {
+      "inputs": {
+        "add_noise": "enable",
+        "noise_seed": 42,
+        "steps": 20,
+        "cfg": 7.5,
+        "sampler_name": "euler",
+        "scheduler": "normal",
+        "start_at_step": 0,
+        "end_at_step": 32,
+        "return_with_leftover_noise": "enable",
+        "model": [
+          "4",
+          0
+        ],
+        "positive": [
+          "6",
+          0
+        ],
+        "negative": [
+          "15",
+          0
+        ],
+        "latent_image": [
+          "5",
+          0
+        ]
+      },
+      "class_type": "KSamplerAdvanced"
+    },
+    "12": {
+      "inputs": {
+        "samples": [
+          "14",
+          0
+        ],
+        "vae": [
+          "4",
+          2
+        ]
+      },
+      "class_type": "VAEDecode"
+    },
+    "13": {
+      "inputs": {
+        "filename_prefix": "test_inference",
+        "images": [
+          "12",
+          0
+        ]
+      },
+      "class_type": "SaveImage"
+    },
+    "14": {
+      "inputs": {
+        "add_noise": "disable",
+        "noise_seed": 42,
+        "steps": 20,
+        "cfg": 7.5,
+        "sampler_name": "euler",
+        "scheduler": "normal",
+        "start_at_step": 32,
+        "end_at_step": 10000,
+        "return_with_leftover_noise": "disable",
+        "model": [
+          "16",
+          0
+        ],
+        "positive": [
+          "17",
+          0
+        ],
+        "negative": [
+          "20",
+          0
+        ],
+        "latent_image": [
+          "10",
+          0
+        ]
+      },
+      "class_type": "KSamplerAdvanced"
+    },
+    "15": {
+      "inputs": {
+        "conditioning": [
+          "6",
+          0
+        ]
+      },
+      "class_type": "ConditioningZeroOut"
+    },
+    "16": {
+      "inputs": {
+        "ckpt_name": "sd_xl_refiner_1.0.safetensors"
+      },
+      "class_type": "CheckpointLoaderSimple"
+    },
+    "17": {
+      "inputs": {
+        "text": "a photo of a cat",
+        "clip": [
+          "16",
+          1
+        ]
+      },
+      "class_type": "CLIPTextEncode"
+    },
+    "20": {
+      "inputs": {
+        "text": "",
+        "clip": [
+          "16",
+          1
+        ]
+      },
+      "class_type": "CLIPTextEncode"
+    }
+  }

tests/inference/test_inference.py

@@ -0,0 +1,239 @@
+from copy import deepcopy
+from io import BytesIO
+from urllib import request
+import numpy
+import os
+from PIL import Image
+import pytest
+from pytest import fixture
+import time
+import torch
+from typing import Union
+import json
+import subprocess
+import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
+import uuid
+import urllib.request
+import urllib.parse
+
+
+from comfy.samplers import KSampler
+
+"""
+These tests generate and save images through a range of parameters
+"""
+
+class ComfyGraph:
+    def __init__(self, 
+                 graph: dict,
+                 sampler_nodes: list[str],
+                 ):
+        self.graph = graph
+        self.sampler_nodes = sampler_nodes
+
+    def set_prompt(self, prompt, negative_prompt=None):
+        # Sets the prompt for the sampler nodes (eg. base and refiner)
+        for node in self.sampler_nodes:
+            prompt_node = self.graph[node]['inputs']['positive'][0]
+            self.graph[prompt_node]['inputs']['text'] = prompt
+            if negative_prompt:
+                negative_prompt_node = self.graph[node]['inputs']['negative'][0]
+                self.graph[negative_prompt_node]['inputs']['text'] = negative_prompt
+
+    def set_sampler_name(self, sampler_name:str, ):
+        # sets the sampler name for the sampler nodes (eg. base and refiner)
+        for node in self.sampler_nodes:
+            self.graph[node]['inputs']['sampler_name'] = sampler_name
+    
+    def set_scheduler(self, scheduler:str):
+        # sets the sampler name for the sampler nodes (eg. base and refiner)
+        for node in self.sampler_nodes:
+            self.graph[node]['inputs']['scheduler'] = scheduler
+    
+    def set_filename_prefix(self, prefix:str):
+        # sets the filename prefix for the save nodes
+        for node in self.graph:
+            if self.graph[node]['class_type'] == 'SaveImage':
+                self.graph[node]['inputs']['filename_prefix'] = prefix
+
+
+class ComfyClient:
+    # From examples/websockets_api_example.py
+
+    def connect(self, 
+                    listen:str = '127.0.0.1', 
+                    port:Union[str,int] = 8188,
+                    client_id: str = str(uuid.uuid4())
+                    ):
+        self.client_id = client_id
+        self.server_address = f"{listen}:{port}"
+        ws = websocket.WebSocket()
+        ws.connect("ws://{}/ws?clientId={}".format(self.server_address, self.client_id))
+        self.ws = ws
+
+    def queue_prompt(self, prompt):
+        p = {"prompt": prompt, "client_id": self.client_id}
+        data = json.dumps(p).encode('utf-8')
+        req =  urllib.request.Request("http://{}/prompt".format(self.server_address), data=data)
+        return json.loads(urllib.request.urlopen(req).read())
+
+    def get_image(self, filename, subfolder, folder_type):
+        data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
+        url_values = urllib.parse.urlencode(data)
+        with urllib.request.urlopen("http://{}/view?{}".format(self.server_address, url_values)) as response:
+            return response.read()
+
+    def get_history(self, prompt_id):
+        with urllib.request.urlopen("http://{}/history/{}".format(self.server_address, prompt_id)) as response:
+            return json.loads(response.read())
+
+    def get_images(self, graph, save=True):
+        prompt = graph
+        if not save:
+            # Replace save nodes with preview nodes
+            prompt_str = json.dumps(prompt)
+            prompt_str = prompt_str.replace('SaveImage', 'PreviewImage')
+            prompt = json.loads(prompt_str)
+
+        prompt_id = self.queue_prompt(prompt)['prompt_id']
+        output_images = {}
+        while True:
+            out = self.ws.recv()
+            if isinstance(out, str):
+                message = json.loads(out)
+                if message['type'] == 'executing':
+                    data = message['data']
+                    if data['node'] is None and data['prompt_id'] == prompt_id:
+                        break #Execution is done
+            else:
+                continue #previews are binary data
+
+        history = self.get_history(prompt_id)[prompt_id]
+        for o in history['outputs']:
+            for node_id in history['outputs']:
+                node_output = history['outputs'][node_id]
+                if 'images' in node_output:
+                    images_output = []
+                    for image in node_output['images']:
+                        image_data = self.get_image(image['filename'], image['subfolder'], image['type'])
+                        images_output.append(image_data)
+                output_images[node_id] = images_output
+
+        return output_images
+
+#
+# Initialize graphs
+#
+default_graph_file = 'tests/inference/graphs/default_graph_sdxl1_0.json'
+with open(default_graph_file, 'r') as file:
+    default_graph = json.loads(file.read())
+DEFAULT_COMFY_GRAPH = ComfyGraph(graph=default_graph, sampler_nodes=['10','14'])
+DEFAULT_COMFY_GRAPH_ID = os.path.splitext(os.path.basename(default_graph_file))[0]
+
+#
+# Loop through these variables
+#
+comfy_graph_list = [DEFAULT_COMFY_GRAPH]
+comfy_graph_ids = [DEFAULT_COMFY_GRAPH_ID]
+prompt_list = [
+    'a painting of a cat',
+]
+
+sampler_list = KSampler.SAMPLERS
+scheduler_list = KSampler.SCHEDULERS
+
+@pytest.mark.inference
+@pytest.mark.parametrize("sampler", sampler_list)
+@pytest.mark.parametrize("scheduler", scheduler_list)
+@pytest.mark.parametrize("prompt", prompt_list)
+class TestInference:
+    #
+    # Initialize server and client
+    #
+    @fixture(scope="class", autouse=True)
+    def _server(self, args_pytest):
+        # Start server
+        p = subprocess.Popen([
+                'python','main.py', 
+                '--output-directory', args_pytest["output_dir"],
+                '--listen', args_pytest["listen"],
+                '--port', str(args_pytest["port"]),
+                ])
+        yield
+        p.kill()
+        torch.cuda.empty_cache()
+
+    def start_client(self, listen:str, port:int):
+        # Start client
+        comfy_client = ComfyClient()
+        # Connect to server (with retries)
+        n_tries = 5
+        for i in range(n_tries):
+            time.sleep(4)
+            try:
+                comfy_client.connect(listen=listen, port=port)
+            except ConnectionRefusedError as e:
+                print(e)
+                print(f"({i+1}/{n_tries}) Retrying...")
+            else:
+                break
+        return comfy_client
+
+    #
+    # Client and graph fixtures with server warmup
+    #
+    # Returns a "_client_graph", which is client-graph pair corresponding to an initialized server
+    # The "graph" is the default graph
+    @fixture(scope="class", params=comfy_graph_list, ids=comfy_graph_ids, autouse=True)
+    def _client_graph(self, request, args_pytest, _server) -> (ComfyClient, ComfyGraph):
+        comfy_graph = request.param
+        
+        # Start client
+        comfy_client = self.start_client(args_pytest["listen"], args_pytest["port"])
+
+        # Warm up pipeline
+        comfy_client.get_images(graph=comfy_graph.graph, save=False)
+
+        yield comfy_client, comfy_graph
+        del comfy_client
+        del comfy_graph
+        torch.cuda.empty_cache()
+
+    @fixture
+    def client(self, _client_graph):
+        client = _client_graph[0]
+        yield client
+    
+    @fixture
+    def comfy_graph(self, _client_graph):
+        # avoid mutating the graph
+        graph = deepcopy(_client_graph[1])
+        yield graph
+
+    def test_comfy(
+        self,
+        client,
+        comfy_graph,
+        sampler,
+        scheduler,
+        prompt,
+        request
+    ):
+        test_info = request.node.name
+        comfy_graph.set_filename_prefix(test_info)
+        # Settings for comfy graph
+        comfy_graph.set_sampler_name(sampler)
+        comfy_graph.set_scheduler(scheduler)
+        comfy_graph.set_prompt(prompt)
+
+        # Generate
+        images = client.get_images(comfy_graph.graph)
+
+        assert len(images) != 0, "No images generated"
+        # assert all images are not blank
+        for images_output in images.values():
+            for image_data in images_output:
+                pil_image = Image.open(BytesIO(image_data))
+                assert numpy.array(pil_image).any() != 0, "Image is blank"
+
+

web/lib/litegraph.core.js

@@ -4928,7 +4928,9 @@ LGraphNode.prototype.executeAction = function(action)
         this.title = o.title;
         this._bounding.set(o.bounding);
         this.color = o.color;
-        this.font = o.font;
+        if (o.font_size) {
+            this.font_size = o.font_size;
+        }
     };
 
     LGraphGroup.prototype.serialize = function() {
@@ -4942,7 +4944,7 @@ LGraphNode.prototype.executeAction = function(action)
                 Math.round(b[3])
             ],
             color: this.color,
-            font: this.font
+            font_size: this.font_size
         };
     };
 

web/scripts/app.js

@@ -532,7 +532,17 @@ export class ComfyApp {
 								}
 							}
 							this.imageRects.push([x, y, cellWidth, cellHeight]);
-							ctx.drawImage(img, x, y, cellWidth, cellHeight);
+
+							let wratio = cellWidth/img.width;
+							let hratio = cellHeight/img.height;
+							var ratio = Math.min(wratio, hratio);
+
+							let imgHeight = ratio * img.height;
+							let imgY = row * cellHeight + shiftY + (cellHeight - imgHeight)/2;
+							let imgWidth = ratio * img.width;
+							let imgX = col * cellWidth + shiftX + (cellWidth - imgWidth)/2;
+
+							ctx.drawImage(img, imgX, imgY, imgWidth, imgHeight);
 							ctx.filter = "none";
 						}
 
@@ -743,8 +753,9 @@ export class ComfyApp {
 				// Default system copy
 				return;
 			}
+
 			// copy nodes and clear clipboard
-			if (this.canvas.selected_nodes) {
+			if (e.target.className === "litegraph" && this.canvas.selected_nodes) {
 				this.canvas.copyToClipboard();
 				e.clipboardData.setData('text', ' '); //clearData doesn't remove images from clipboard
 				e.preventDefault();
@@ -1297,7 +1308,13 @@ export class ComfyApp {
 
 		let reset_invalid_values = false;
 		if (!graphData) {
-			graphData = structuredClone(defaultGraph);
+			if (typeof structuredClone === "undefined")
+			{
+				graphData = JSON.parse(JSON.stringify(defaultGraph));
+			}else
+			{
+				graphData = structuredClone(defaultGraph);
+			}
 			reset_invalid_values = true;
 		}
 

web/scripts/ui.js

@@ -577,6 +577,25 @@ export class ComfyUI {
 			defaultValue: false,
 		});
 
+		this.settings.addSetting({
+			id: "Comfy.DisableFloatRounding",
+			name: "Disable rounding floats (requires page reload).",
+			type: "boolean",
+			defaultValue: false,
+		});
+
+		this.settings.addSetting({
+			id: "Comfy.FloatRoundingPrecision",
+			name: "Decimal places [0 = auto] (requires page reload).",
+			type: "slider",
+			attrs: {
+				min: 0,
+				max: 6,
+				step: 1,
+			},
+			defaultValue: 0,
+		});
+
 		const fileInput = $el("input", {
 			id: "comfy-file-input",
 			type: "file",

web/scripts/widgets.js

@@ -1,18 +1,23 @@
 import { api } from "./api.js"
 
-function getNumberDefaults(inputData, defaultStep) {
+function getNumberDefaults(inputData, defaultStep, precision, enable_rounding) {
 	let defaultVal = inputData[1]["default"];
-	let { min, max, step } = inputData[1];
+	let { min, max, step, round} = inputData[1];
 
 	if (defaultVal == undefined) defaultVal = 0;
 	if (min == undefined) min = 0;
 	if (max == undefined) max = 2048;
 	if (step == undefined) step = defaultStep;
-// precision is the number of decimal places to show. 
+	// precision is the number of decimal places to show.
-// by default, display the the smallest number of decimal places such that changes of size step are visible.
+	// by default, display the the smallest number of decimal places such that changes of size step are visible.
-	let precision = Math.max(-Math.floor(Math.log10(step)),0)
+	if (precision == undefined) {
-// by default, round the value to those decimal places shown.
+		precision = Math.max(-Math.floor(Math.log10(step)),0);
-	let round = Math.round(1000000*Math.pow(0.1,precision))/1000000;
+	}
+
+	if (enable_rounding && (round == undefined || round === true)) {
+		// by default, round the value to those decimal places shown.
+		round = Math.round(1000000*Math.pow(0.1,precision))/1000000;
+	}
 
 	return { val: defaultVal, config: { min, max, step: 10.0 * step, round, precision } };
 }
@@ -268,15 +273,22 @@ export const ComfyWidgets = {
 	"INT:noise_seed": seedWidget,
 	FLOAT(node, inputName, inputData, app) {
 		let widgetType = isSlider(inputData[1]["display"], app);
-		const { val, config } = getNumberDefaults(inputData, 0.5);
+		let precision = app.ui.settings.getSettingValue("Comfy.FloatRoundingPrecision");
+		let disable_rounding = app.ui.settings.getSettingValue("Comfy.DisableFloatRounding")
+		if (precision == 0) precision = undefined;
+		const { val, config } = getNumberDefaults(inputData, 0.5, precision, !disable_rounding);
 		return { widget: node.addWidget(widgetType, inputName, val, 
 			function (v) {
-				this.value = Math.round(v/config.round)*config.round;
+				if (config.round) {
+					this.value = Math.round(v/config.round)*config.round;
+				} else {
+					this.value = v;
+				}
 			}, config) };
 	},
 	INT(node, inputName, inputData, app) {
 		let widgetType = isSlider(inputData[1]["display"], app);
-		const { val, config } = getNumberDefaults(inputData, 1);
+		const { val, config } = getNumberDefaults(inputData, 1, 0, true);
 		Object.assign(config, { precision: 0 });
 		return {
 			widget: node.addWidget(