Merge pull request #2068 from lllyasviel/dev0127

2.1.864

.gitignore

@@ -20,6 +20,7 @@ user_path_config.txt
 user_path_config-deprecated.txt
 /modules/*.png
 /repositories
+/fooocus_env
 /venv
 /tmp
 /ui-config.json

args_manager.py

@@ -20,6 +20,12 @@
 args_parser.parser.add_argument("--disable-analytics", action='store_true',
                                 help="Disables analytics for Gradio", default=False)
 
+args_parser.parser.add_argument("--disable-preset-download", action='store_true',
+                                help="Disables downloading models for presets", default=False)
+
+args_parser.parser.add_argument("--always-download-new-model", action='store_true',
+                                help="Always download newer models ", default=False)
+
 args_parser.parser.set_defaults(
     disable_cuda_malloc=True,
     in_browser=True,

fooocus_version.py

@@ -1 +1 @@
-version = '2.1.862'
+version = '2.1.864'

launch.py

@@ -21,8 +21,7 @@
 from build_launcher import build_launcher
 from modules.launch_util import is_installed, run, python, run_pip, requirements_met
 from modules.model_loader import load_file_from_url
-from modules.config import path_checkpoints, path_loras, path_vae_approx, path_fooocus_expansion, \
-    checkpoint_downloads, path_embeddings, embeddings_downloads, lora_downloads
+from modules import config
 
 
 REINSTALL_ALL = False
@@ -70,25 +69,6 @@ def prepare_environment():
 ]
 
 
-def download_models():
-    for file_name, url in checkpoint_downloads.items():
-        load_file_from_url(url=url, model_dir=path_checkpoints, file_name=file_name)
-    for file_name, url in embeddings_downloads.items():
-        load_file_from_url(url=url, model_dir=path_embeddings, file_name=file_name)
-    for file_name, url in lora_downloads.items():
-        load_file_from_url(url=url, model_dir=path_loras, file_name=file_name)
-    for file_name, url in vae_approx_filenames:
-        load_file_from_url(url=url, model_dir=path_vae_approx, file_name=file_name)
-
-    load_file_from_url(
-        url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_expansion.bin',
-        model_dir=path_fooocus_expansion,
-        file_name='pytorch_model.bin'
-    )
-
-    return
-
-
 def ini_args():
     from args_manager import args
     return args
@@ -104,6 +84,43 @@ def ini_args():
     print("Set device to:", args.gpu_device_id)
 
 
+def download_models():
+    for file_name, url in vae_approx_filenames:
+        load_file_from_url(url=url, model_dir=config.path_vae_approx, file_name=file_name)
+
+    load_file_from_url(
+        url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_expansion.bin',
+        model_dir=config.path_fooocus_expansion,
+        file_name='pytorch_model.bin'
+    )
+
+    if args.disable_preset_download:
+        print('Skipped model download.')
+        return
+
+    if not args.always_download_new_model:
+        if not os.path.exists(os.path.join(config.path_checkpoints, config.default_base_model_name)):
+            for alternative_model_name in config.previous_default_models:
+                if os.path.exists(os.path.join(config.path_checkpoints, alternative_model_name)):
+                    print(f'You do not have [{config.default_base_model_name}] but you have [{alternative_model_name}].')
+                    print(f'Fooocus will use [{alternative_model_name}] to avoid downloading new models, '
+                          f'but you are not using latest models.')
+                    print('Use --always-download-new-model to avoid fallback and always get new models.')
+                    config.checkpoint_downloads = {}
+                    config.default_base_model_name = alternative_model_name
+                    break
+
+    for file_name, url in config.checkpoint_downloads.items():
+        load_file_from_url(url=url, model_dir=config.path_checkpoints, file_name=file_name)
+    for file_name, url in config.embeddings_downloads.items():
+        load_file_from_url(url=url, model_dir=config.path_embeddings, file_name=file_name)
+    for file_name, url in config.lora_downloads.items():
+        load_file_from_url(url=url, model_dir=config.path_loras, file_name=file_name)
+
+    return
+
+
 download_models()
 
+
 from webui import *

ldm_patched/contrib/external.py

@@ -361,6 +361,62 @@ def encode(self, vae, pixels, mask, grow_mask_by=6):
 
         return ({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())}, )
 
+
+class InpaintModelConditioning:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "vae": ("VAE", ),
+                             "pixels": ("IMAGE", ),
+                             "mask": ("MASK", ),
+                             }}
+
+    RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT")
+    RETURN_NAMES = ("positive", "negative", "latent")
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning/inpaint"
+
+    def encode(self, positive, negative, pixels, vae, mask):
+        x = (pixels.shape[1] // 8) * 8
+        y = (pixels.shape[2] // 8) * 8
+        mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
+
+        orig_pixels = pixels
+        pixels = orig_pixels.clone()
+        if pixels.shape[1] != x or pixels.shape[2] != y:
+            x_offset = (pixels.shape[1] % 8) // 2
+            y_offset = (pixels.shape[2] % 8) // 2
+            pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
+            mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset]
+
+        m = (1.0 - mask.round()).squeeze(1)
+        for i in range(3):
+            pixels[:,:,:,i] -= 0.5
+            pixels[:,:,:,i] *= m
+            pixels[:,:,:,i] += 0.5
+        concat_latent = vae.encode(pixels)
+        orig_latent = vae.encode(orig_pixels)
+
+        out_latent = {}
+
+        out_latent["samples"] = orig_latent
+        out_latent["noise_mask"] = mask
+
+        out = []
+        for conditioning in [positive, negative]:
+            c = []
+            for t in conditioning:
+                d = t[1].copy()
+                d["concat_latent_image"] = concat_latent
+                d["concat_mask"] = mask
+                n = [t[0], d]
+                c.append(n)
+            out.append(c)
+        return (out[0], out[1], out_latent)
+
+
 class SaveLatent:
     def __init__(self):
         self.output_dir = ldm_patched.utils.path_utils.get_output_directory()
@@ -1417,6 +1473,8 @@ def load_image(self, image):
         output_masks = []
         for i in ImageSequence.Iterator(img):
             i = ImageOps.exif_transpose(i)
+            if i.mode == 'I':
+                i = i.point(lambda i: i * (1 / 255))
             image = i.convert("RGB")
             image = np.array(image).astype(np.float32) / 255.0
             image = torch.from_numpy(image)[None,]
@@ -1472,6 +1530,8 @@ def load_image(self, image, channel):
         i = Image.open(image_path)
         i = ImageOps.exif_transpose(i)
         if i.getbands() != ("R", "G", "B", "A"):
+            if i.mode == 'I':
+                i = i.point(lambda i: i * (1 / 255))
             i = i.convert("RGBA")
         mask = None
         c = channel[0].upper()
@@ -1626,10 +1686,11 @@ def INPUT_TYPES(s):
     def expand_image(self, image, left, top, right, bottom, feathering):
         d1, d2, d3, d4 = image.size()
 
-        new_image = torch.zeros(
+        new_image = torch.ones(
             (d1, d2 + top + bottom, d3 + left + right, d4),
             dtype=torch.float32,
-        )
+        ) * 0.5
+
         new_image[:, top:top + d2, left:left + d3, :] = image
 
         mask = torch.ones(
@@ -1721,6 +1782,7 @@ def expand_image(self, image, left, top, right, bottom, feathering):
     "unCLIPCheckpointLoader": unCLIPCheckpointLoader,
     "GLIGENLoader": GLIGENLoader,
     "GLIGENTextBoxApply": GLIGENTextBoxApply,
+    "InpaintModelConditioning": InpaintModelConditioning,
 
     "CheckpointLoader": CheckpointLoader,
     "DiffusersLoader": DiffusersLoader,
@@ -1882,6 +1944,8 @@ def init_custom_nodes():
         "nodes_sag.py",
         "nodes_perpneg.py",
         "nodes_stable3d.py",
+        "nodes_sdupscale.py",
+        "nodes_photomaker.py",
     ]
 
     for node_file in extras_files:

ldm_patched/contrib/external_custom_sampler.py

@@ -15,15 +15,22 @@ def INPUT_TYPES(s):
                     {"model": ("MODEL",),
                      "scheduler": (ldm_patched.modules.samplers.SCHEDULER_NAMES, ),
                      "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                       }
                }
     RETURN_TYPES = ("SIGMAS",)
     CATEGORY = "sampling/custom_sampling/schedulers"
 
     FUNCTION = "get_sigmas"
 
-    def get_sigmas(self, model, scheduler, steps):
-        sigmas = ldm_patched.modules.samplers.calculate_sigmas_scheduler(model.model, scheduler, steps).cpu()
+    def get_sigmas(self, model, scheduler, steps, denoise):
+        total_steps = steps
+        if denoise < 1.0:
+            total_steps = int(steps/denoise)
+
+        ldm_patched.modules.model_management.load_models_gpu([model])
+        sigmas = ldm_patched.modules.samplers.calculate_sigmas_scheduler(model.model, scheduler, total_steps).cpu()
+        sigmas = sigmas[-(steps + 1):]
         return (sigmas, )
 
 
@@ -100,6 +107,7 @@ def INPUT_TYPES(s):
     def get_sigmas(self, model, steps, denoise):
         start_step = 10 - int(10 * denoise)
         timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps]
+        ldm_patched.modules.model_management.load_models_gpu([model])
         sigmas = model.model.model_sampling.sigma(timesteps)
         sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
         return (sigmas, )

ldm_patched/contrib/external_freelunch.py

@@ -36,7 +36,7 @@ def INPUT_TYPES(s):
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "model_patches"
 
     def patch(self, model, b1, b2, s1, s2):
         model_channels = model.model.model_config.unet_config["model_channels"]
@@ -75,7 +75,7 @@ def INPUT_TYPES(s):
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "model_patches"
 
     def patch(self, model, b1, b2, s1, s2):
         model_channels = model.model.model_config.unet_config["model_channels"]

ldm_patched/contrib/external_hypertile.py

@@ -34,29 +34,29 @@ def INPUT_TYPES(s):
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "model_patches"
 
     def patch(self, model, tile_size, swap_size, max_depth, scale_depth):
         model_channels = model.model.model_config.unet_config["model_channels"]
 
-        apply_to = set()
-        temp = model_channels
-        for x in range(max_depth + 1):
-            apply_to.add(temp)
-            temp *= 2
-
         latent_tile_size = max(32, tile_size) // 8
         self.temp = None
 
         def hypertile_in(q, k, v, extra_options):
-            if q.shape[-1] in apply_to:
+            model_chans = q.shape[-2]
+            orig_shape = extra_options['original_shape']
+            apply_to = []
+            for i in range(max_depth + 1):
+                apply_to.append((orig_shape[-2] / (2 ** i)) * (orig_shape[-1] / (2 ** i)))
+
+            if model_chans in apply_to:
                 shape = extra_options["original_shape"]
                 aspect_ratio = shape[-1] / shape[-2]
 
                 hw = q.size(1)
                 h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio))
 
-                factor = 2**((q.shape[-1] // model_channels) - 1) if scale_depth else 1
+                factor = (2 ** apply_to.index(model_chans)) if scale_depth else 1
                 nh = random_divisor(h, latent_tile_size * factor, swap_size)
                 nw = random_divisor(w, latent_tile_size * factor, swap_size)
 

ldm_patched/contrib/external_latent.py

@@ -124,10 +124,34 @@ def batch(self, samples1, samples2):
         samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])])
         return (samples_out,)
 
+class LatentBatchSeedBehavior:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples": ("LATENT",),
+                              "seed_behavior": (["random", "fixed"],),}}
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "op"
+
+    CATEGORY = "latent/advanced"
+
+    def op(self, samples, seed_behavior):
+        samples_out = samples.copy()
+        latent = samples["samples"]
+        if seed_behavior == "random":
+            if 'batch_index' in samples_out:
+                samples_out.pop('batch_index')
+        elif seed_behavior == "fixed":
+            batch_number = samples_out.get("batch_index", [0])[0]
+            samples_out["batch_index"] = [batch_number] * latent.shape[0]
+
+        return (samples_out,)
+
 NODE_CLASS_MAPPINGS = {
     "LatentAdd": LatentAdd,
     "LatentSubtract": LatentSubtract,
     "LatentMultiply": LatentMultiply,
     "LatentInterpolate": LatentInterpolate,
     "LatentBatch": LatentBatch,
+    "LatentBatchSeedBehavior": LatentBatchSeedBehavior,
 }