mirror of
https://github.com/nod-ai/SHARK-Studio.git
synced 2026-01-08 21:38:04 -05:00
Many UI fixes and controlnet impovements (#2025)
* multi-controlnet UI and perf fixes * Controlnet fixes
This commit is contained in:
Ean Garvey
@@ -190,9 +190,6 @@ class SharkifyStableDiffusionModel:
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)
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self.model_id = model_id if custom_weights == "" else custom_weights
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# TODO: remove the following line when stable-diffusion-2-1 works
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if self.model_id == "stabilityai/stable-diffusion-2-1":
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self.model_id = "stabilityai/stable-diffusion-2-1-base"
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self.custom_vae = custom_vae
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self.precision = precision
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self.base_vae = use_base_vae
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@@ -208,6 +205,7 @@ class SharkifyStableDiffusionModel:
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+ "_"
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+ precision
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)
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self.model_namedata = self.model_name
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print(f"use_tuned? sharkify: {use_tuned}")
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self.use_tuned = use_tuned
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if use_tuned:
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@@ -272,7 +270,11 @@ class SharkifyStableDiffusionModel:
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stencil_names = []
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for i, stencil in enumerate(self.stencils):
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if stencil is not None:
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cnet_config = model_config + stencil.split("_")[-1]
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cnet_config = (
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self.model_namedata
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+ "_v1-5"
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+ stencil.split("_")[-1]
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)
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stencil_names.append(
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get_extended_name(sub_model + cnet_config)
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)
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@@ -539,7 +541,7 @@ class SharkifyStableDiffusionModel:
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)
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if use_lora != "":
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update_lora_weight(self.unet, use_lora, "unet")
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self.in_channels = self.unet.in_channels
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self.in_channels = self.unet.config.in_channels
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self.train(False)
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def forward(
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@@ -765,10 +767,11 @@ class SharkifyStableDiffusionModel:
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model_name = "stencil_adapter_512" if use_large else "stencil_adapter"
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ext_model_name = self.model_name[model_name]
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if isinstance(ext_model_name, list):
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desired_name = None
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print(ext_model_name)
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for i in ext_model_name:
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if stencil_id.split("_")[-1] in i:
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desired_name = i
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print(f"Multi-CN: compiling model {i}")
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else:
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continue
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if desired_name:
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@@ -162,6 +162,7 @@ class Image2ImagePipeline(StableDiffusionPipeline):
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images,
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resample_type,
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control_mode,
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preprocessed_hints=[],
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):
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# prompts and negative prompts must be a list.
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if isinstance(prompts, str):
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@@ -25,7 +25,10 @@ from apps.stable_diffusion.src.schedulers import SharkEulerDiscreteScheduler
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from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
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StableDiffusionPipeline,
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)
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from apps.stable_diffusion.src.utils import controlnet_hint_conversion
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from apps.stable_diffusion.src.utils import (
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controlnet_hint_conversion,
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controlnet_hint_reshaping,
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)
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from apps.stable_diffusion.src.utils import (
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start_profiling,
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end_profiling,
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@@ -213,7 +216,7 @@ class StencilPipeline(StableDiffusionPipeline):
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)
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for i, controlnet_hint in enumerate(stencil_hints):
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if controlnet_hint is None:
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continue
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pass
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if text_embeddings.shape[1] <= self.model_max_length:
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control = self.controlnet[i](
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"forward",
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@@ -300,7 +303,6 @@ class StencilPipeline(StableDiffusionPipeline):
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send_to_host=False,
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)
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else:
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print(self.unet_512)
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noise_pred = self.unet_512(
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"forward",
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(
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@@ -389,13 +391,31 @@ class StencilPipeline(StableDiffusionPipeline):
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stencil_images,
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resample_type,
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control_mode,
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preprocessed_hints,
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):
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# Control Embedding check & conversion
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# controlnet_hint = controlnet_hint_conversion(
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# image, use_stencil, height, width, dtype, num_images_per_prompt=1
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# )
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stencil_hints = []
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for i, hint in enumerate(preprocessed_hints):
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if hint is not None:
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hint = controlnet_hint_reshaping(
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hint,
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height,
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width,
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dtype,
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num_images_per_prompt=1,
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)
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stencil_hints.append(hint)
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for i, stencil in enumerate(stencils):
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if stencil == None:
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continue
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if len(stencil_hints) >= i:
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if stencil_hints[i] is not None:
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print(f"Using preprocessed controlnet hint for {stencil}")
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continue
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image = stencil_images[i]
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stencil_hints.append(
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controlnet_hint_conversion(
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@@ -13,6 +13,7 @@ from apps.stable_diffusion.src.utils.sd_annotation import sd_model_annotation
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from apps.stable_diffusion.src.utils.stable_args import args
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from apps.stable_diffusion.src.utils.stencils.stencil_utils import (
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controlnet_hint_conversion,
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controlnet_hint_reshaping,
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get_stencil_model_id,
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)
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from apps.stable_diffusion.src.utils.utils import (
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@@ -20,9 +20,7 @@ def save_img(img):
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get_generated_imgs_todays_subdir,
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)
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subdir = Path(
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get_generated_imgs_path(), get_generated_imgs_todays_subdir()
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)
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subdir = Path(get_generated_imgs_path(), "preprocessed_control_hints")
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os.makedirs(subdir, exist_ok=True)
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if isinstance(img, Image.Image):
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img.save(
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@@ -60,7 +58,7 @@ def HWC3(x):
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return y
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def controlnet_hint_shaping(
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def controlnet_hint_reshaping(
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controlnet_hint, height, width, dtype, num_images_per_prompt=1
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):
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channels = 3
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@@ -79,7 +77,7 @@ def controlnet_hint_shaping(
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)
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return controlnet_hint
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else:
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return controlnet_hint_shaping(
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return controlnet_hint_reshaping(
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Image.fromarray(controlnet_hint.detach().numpy()),
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height,
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width,
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@@ -111,7 +109,7 @@ def controlnet_hint_shaping(
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) # b h w c -> b c h w
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return controlnet_hint
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else:
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return controlnet_hint_shaping(
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return controlnet_hint_reshaping(
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Image.fromarray(controlnet_hint),
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height,
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width,
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@@ -128,14 +126,16 @@ def controlnet_hint_shaping(
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np.float16
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) # to numpy
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controlnet_hint = controlnet_hint[:, :, ::-1] # RGB -> BGR
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return
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return controlnet_hint_reshaping(
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controlnet_hint, height, width, dtype, num_images_per_prompt
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)
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else:
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(hint_w, hint_h) = controlnet_hint.size
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left = int((hint_w - width) / 2)
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right = left + height
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controlnet_hint = controlnet_hint.crop((left, 0, right, hint_h))
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controlnet_hint = controlnet_hint.resize((width, height))
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return controlnet_hint_shaping(
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return controlnet_hint_reshaping(
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controlnet_hint, height, width, dtype, num_images_per_prompt
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)
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else:
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@@ -169,7 +169,7 @@ def controlnet_hint_conversion(
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controlnet_hint = hint_zoedepth(image)
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case _:
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return None
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controlnet_hint = controlnet_hint_shaping(
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controlnet_hint = controlnet_hint_reshaping(
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controlnet_hint, height, width, dtype, num_images_per_prompt
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)
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return controlnet_hint
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@@ -1008,8 +1008,7 @@ def get_generation_text_info(seeds, device):
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# Both width and height should be in the range of [128, 768] and multiple of 8.
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# This utility function performs the transformation on the input image while
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# also maintaining the aspect ratio before sending it to the stencil pipeline.
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def resize_stencil(image: Image.Image):
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width, height = image.size
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def resize_stencil(image: Image.Image, width, height):
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aspect_ratio = width / height
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min_size = min(width, height)
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if min_size < 128:
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@@ -81,6 +81,7 @@ def img2img_inf(
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control_mode: str,
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stencils: list,
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images: list,
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preprocessed_hints: list,
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):
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from apps.stable_diffusion.web.ui.utils import (
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get_custom_model_pathfile,
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@@ -151,7 +152,7 @@ def img2img_inf(
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stencil_count += 1
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if stencil_count > 0:
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args.hf_model_id = "runwayml/stable-diffusion-v1-5"
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image, width, height = resize_stencil(image)
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image, _, _ = resize_stencil(image, width, height)
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elif "Shark" in args.scheduler:
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print(
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f"Shark schedulers are not supported. Switching to EulerDiscrete "
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@@ -195,7 +196,7 @@ def img2img_inf(
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model_id = (
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args.hf_model_id
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if args.hf_model_id
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else "stabilityai/stable-diffusion-1-5-base"
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else "runwayml/stable-diffusion-v1-5"
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)
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global_obj.set_schedulers(get_schedulers(model_id))
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scheduler_obj = global_obj.get_scheduler(args.scheduler)
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@@ -278,6 +279,7 @@ def img2img_inf(
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images,
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resample_type=resample_type,
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control_mode=control_mode,
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preprocessed_hints=preprocessed_hints,
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)
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total_time = time.time() - start_time
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text_output = get_generation_text_info(
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@@ -308,6 +310,7 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
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STENCIL_COUNT = 2
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stencils = gr.State([None] * STENCIL_COUNT)
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images = gr.State([None] * STENCIL_COUNT)
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preprocessed_hints = gr.State([None] * STENCIL_COUNT)
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with gr.Row(elem_id="ui_title"):
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nod_logo = Image.open(nodlogo_loc)
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with gr.Row():
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@@ -374,7 +377,7 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
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img2img_init_image = gr.Image(
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label="Input Image",
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type="pil",
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height=512,
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height=300,
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interactive=True,
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)
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@@ -388,10 +391,23 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
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]
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def cnet_preview(
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model, input_image, index, stencils, images
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model,
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input_image,
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index,
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stencils,
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images,
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preprocessed_hints,
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):
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if isinstance(input_image, PIL.Image.Image):
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img_dict = {
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"background": None,
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"layers": [None],
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"composite": input_image,
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}
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input_image = EditorValue(img_dict)
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images[index] = input_image
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stencils[index] = model
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if model:
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stencils[index] = model
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match model:
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case "canny":
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canny = CannyDetector()
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@@ -400,41 +416,75 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
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100,
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200,
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)
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preprocessed_hints[index] = Image.fromarray(
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result
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)
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return (
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Image.fromarray(result),
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stencils,
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images,
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preprocessed_hints,
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)
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case "openpose":
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openpose = OpenposeDetector()
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result = openpose(
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np.array(input_image["composite"])
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)
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print(result)
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# TODO: This is just an empty canvas, need to draw the candidates (which are in result[1])
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preprocessed_hints[index] = Image.fromarray(
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result[0]
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)
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return (
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Image.fromarray(result[0]),
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stencils,
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images,
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preprocessed_hints,
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)
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case "zoedepth":
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zoedepth = ZoeDetector()
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result = zoedepth(
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np.array(input_image["composite"])
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)
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preprocessed_hints[index] = Image.fromarray(
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result
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)
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return (
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Image.fromarray(result),
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stencils,
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images,
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preprocessed_hints,
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)
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case "scribble":
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preprocessed_hints[index] = input_image[
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"composite"
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]
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return (
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input_image["composite"],
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stencils,
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images,
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preprocessed_hints,
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)
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case _:
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return (None, stencils, images)
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preprocessed_hints[index] = None
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return (
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None,
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stencils,
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images,
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preprocessed_hints,
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)
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def import_original(original_img, width, height):
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resized_img, _, _ = resize_stencil(
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original_img, width, height
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)
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img_dict = {
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"background": resized_img,
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"layers": [resized_img],
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"composite": None,
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}
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return gr.ImageEditor(
|
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value=EditorValue(img_dict),
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crop_size=(width, height),
|
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)
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|
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def create_canvas(width, height):
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data = Image.fromarray(
|
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@@ -451,8 +501,31 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
|
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}
|
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return EditorValue(img_dict)
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|
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def update_cn_input(model, width, height):
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if model == "scribble":
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def update_cn_input(
|
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model,
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width,
|
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height,
|
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stencils,
|
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images,
|
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preprocessed_hints,
|
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index,
|
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):
|
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if model == None:
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stencils[index] = None
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images[index] = None
|
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preprocessed_hints[index] = None
|
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return [
|
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gr.ImageEditor(value=None, visible=False),
|
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gr.Image(value=None),
|
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gr.Slider(visible=False),
|
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gr.Slider(visible=False),
|
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gr.Button(visible=False),
|
||||
gr.Button(visible=False),
|
||||
stencils,
|
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images,
|
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preprocessed_hints,
|
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]
|
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elif model == "scribble":
|
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return [
|
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gr.ImageEditor(
|
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visible=True,
|
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@@ -460,20 +533,25 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
|
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show_label=False,
|
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image_mode="RGB",
|
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type="pil",
|
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value=create_canvas(width, height),
|
||||
brush=Brush(
|
||||
colors=["#000000"], color_mode="fixed"
|
||||
colors=["#000000"],
|
||||
color_mode="fixed",
|
||||
default_size=2,
|
||||
),
|
||||
),
|
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gr.Image(
|
||||
visible=True,
|
||||
show_label=False,
|
||||
interactive=False,
|
||||
interactive=True,
|
||||
show_download_button=False,
|
||||
),
|
||||
gr.Slider(visible=True),
|
||||
gr.Slider(visible=True),
|
||||
gr.Slider(visible=True, label="Canvas Width"),
|
||||
gr.Slider(visible=True, label="Canvas Height"),
|
||||
gr.Button(visible=True),
|
||||
gr.Button(visible=False),
|
||||
stencils,
|
||||
images,
|
||||
preprocessed_hints,
|
||||
]
|
||||
else:
|
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return [
|
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@@ -482,7 +560,6 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
|
||||
image_mode="RGB",
|
||||
type="pil",
|
||||
interactive=True,
|
||||
value=None,
|
||||
),
|
||||
gr.Image(
|
||||
visible=True,
|
||||
@@ -490,9 +567,13 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
|
||||
interactive=True,
|
||||
show_download_button=False,
|
||||
),
|
||||
gr.Slider(visible=False),
|
||||
gr.Slider(visible=False),
|
||||
gr.Slider(visible=True, label="Input Width"),
|
||||
gr.Slider(visible=True, label="Input Height"),
|
||||
gr.Button(visible=False),
|
||||
gr.Button(visible=True),
|
||||
stencils,
|
||||
images,
|
||||
preprocessed_hints,
|
||||
]
|
||||
|
||||
with gr.Row():
|
||||
@@ -525,51 +606,85 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
|
||||
value="Make Canvas!",
|
||||
visible=False,
|
||||
)
|
||||
use_input_img_1 = gr.Button(
|
||||
value="Use Original Image",
|
||||
visible=False,
|
||||
)
|
||||
|
||||
cnet_1_image = gr.ImageEditor(
|
||||
visible=False,
|
||||
image_mode="RGB",
|
||||
interactive=True,
|
||||
show_label=False,
|
||||
show_label=True,
|
||||
label="Input Image",
|
||||
type="pil",
|
||||
)
|
||||
cnet_1_output = gr.Image(
|
||||
visible=True, show_label=False
|
||||
value=None,
|
||||
visible=True,
|
||||
label="Preprocessed Hint",
|
||||
interactive=True,
|
||||
)
|
||||
|
||||
use_input_img_1.click(
|
||||
import_original,
|
||||
[img2img_init_image, canvas_width, canvas_height],
|
||||
[cnet_1_image],
|
||||
)
|
||||
|
||||
cnet_1_model.input(
|
||||
update_cn_input,
|
||||
[cnet_1_model, canvas_width, canvas_height],
|
||||
[
|
||||
fn=(
|
||||
lambda m, w, h, s, i, p: update_cn_input(
|
||||
m, w, h, s, i, p, 0
|
||||
)
|
||||
),
|
||||
inputs=[
|
||||
cnet_1_model,
|
||||
canvas_width,
|
||||
canvas_height,
|
||||
stencils,
|
||||
images,
|
||||
preprocessed_hints,
|
||||
],
|
||||
outputs=[
|
||||
cnet_1_image,
|
||||
cnet_1_output,
|
||||
canvas_width,
|
||||
canvas_height,
|
||||
make_canvas,
|
||||
use_input_img_1,
|
||||
stencils,
|
||||
images,
|
||||
preprocessed_hints,
|
||||
],
|
||||
)
|
||||
make_canvas.click(
|
||||
update_cn_input,
|
||||
[cnet_1_model, canvas_width, canvas_height],
|
||||
create_canvas,
|
||||
[canvas_width, canvas_height],
|
||||
[
|
||||
cnet_1_image,
|
||||
cnet_1_output,
|
||||
canvas_width,
|
||||
canvas_height,
|
||||
make_canvas,
|
||||
],
|
||||
)
|
||||
cnet_1.click(
|
||||
gr.on(
|
||||
triggers=[cnet_1.click],
|
||||
fn=(
|
||||
lambda a, b, s, i: cnet_preview(a, b, 0, s, i)
|
||||
lambda a, b, s, i, p: cnet_preview(
|
||||
a, b, 0, s, i, p
|
||||
)
|
||||
),
|
||||
inputs=[
|
||||
cnet_1_model,
|
||||
cnet_1_image,
|
||||
stencils,
|
||||
images,
|
||||
preprocessed_hints,
|
||||
],
|
||||
outputs=[
|
||||
cnet_1_output,
|
||||
stencils,
|
||||
images,
|
||||
preprocessed_hints,
|
||||
],
|
||||
outputs=[cnet_1_output, stencils, images],
|
||||
)
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
@@ -601,49 +716,81 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
|
||||
value="Make Canvas!",
|
||||
visible=False,
|
||||
)
|
||||
use_input_img_2 = gr.Button(
|
||||
value="Use Original Image",
|
||||
visible=False,
|
||||
)
|
||||
cnet_2_image = gr.ImageEditor(
|
||||
visible=False,
|
||||
image_mode="RGB",
|
||||
interactive=True,
|
||||
show_label=False,
|
||||
type="pil",
|
||||
show_label=True,
|
||||
label="Input Image",
|
||||
)
|
||||
use_input_img_2.click(
|
||||
import_original,
|
||||
[img2img_init_image, canvas_width, canvas_height],
|
||||
[cnet_2_image],
|
||||
)
|
||||
cnet_2_output = gr.Image(
|
||||
visible=True, show_label=False
|
||||
value=None,
|
||||
visible=True,
|
||||
label="Preprocessed Hint",
|
||||
interactive=True,
|
||||
)
|
||||
cnet_2_model.select(
|
||||
update_cn_input,
|
||||
[cnet_2_model, canvas_width, canvas_height],
|
||||
[
|
||||
fn=(
|
||||
lambda m, w, h, s, i, p: update_cn_input(
|
||||
m, w, h, s, i, p, 0
|
||||
)
|
||||
),
|
||||
inputs=[
|
||||
cnet_2_model,
|
||||
canvas_width,
|
||||
canvas_height,
|
||||
stencils,
|
||||
images,
|
||||
preprocessed_hints,
|
||||
],
|
||||
outputs=[
|
||||
cnet_2_image,
|
||||
cnet_2_output,
|
||||
canvas_width,
|
||||
canvas_height,
|
||||
make_canvas,
|
||||
use_input_img_2,
|
||||
stencils,
|
||||
images,
|
||||
preprocessed_hints,
|
||||
],
|
||||
)
|
||||
make_canvas.click(
|
||||
update_cn_input,
|
||||
[cnet_2_model, canvas_width, canvas_height],
|
||||
create_canvas,
|
||||
[canvas_width, canvas_height],
|
||||
[
|
||||
cnet_2_image,
|
||||
cnet_2_output,
|
||||
canvas_width,
|
||||
canvas_height,
|
||||
make_canvas,
|
||||
],
|
||||
)
|
||||
cnet_2.click(
|
||||
fn=(
|
||||
lambda a, b, s, i: cnet_preview(a, b, 1, s, i)
|
||||
lambda a, b, s, i, p: cnet_preview(
|
||||
a, b, 1, s, i, p
|
||||
)
|
||||
),
|
||||
inputs=[
|
||||
cnet_2_model,
|
||||
cnet_2_image,
|
||||
stencils,
|
||||
images,
|
||||
preprocessed_hints,
|
||||
],
|
||||
outputs=[
|
||||
cnet_2_output,
|
||||
stencils,
|
||||
images,
|
||||
preprocessed_hints,
|
||||
],
|
||||
outputs=[cnet_2_output, stencils, images],
|
||||
)
|
||||
control_mode = gr.Radio(
|
||||
choices=["Prompt", "Balanced", "Controlnet"],
|
||||
@@ -865,6 +1012,7 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
|
||||
control_mode,
|
||||
stencils,
|
||||
images,
|
||||
preprocessed_hints,
|
||||
],
|
||||
outputs=[
|
||||
img2img_gallery,
|
||||
|
||||
@@ -348,14 +348,14 @@ with gr.Blocks(title="Text-to-Image-SDXL", theme=theme) as txt2img_sdxl_web:
|
||||
scheduler = gr.Dropdown(
|
||||
elem_id="scheduler",
|
||||
label="Scheduler",
|
||||
value=args.scheduler,
|
||||
value="EulerDiscrete",
|
||||
choices=[
|
||||
"DDIM",
|
||||
"EulerAncestralDiscrete",
|
||||
"EulerDiscrete",
|
||||
"LCMScheduler",
|
||||
],
|
||||
allow_custom_value=False,
|
||||
allow_custom_value=True,
|
||||
visible=True,
|
||||
)
|
||||
with gr.Column():
|
||||
|
||||
@@ -307,10 +307,7 @@ def get_config_from_json(model_ckpt_or_id, jsonconfig):
|
||||
|
||||
|
||||
def default_config_exists(model_ckpt_or_id):
|
||||
if model_ckpt_or_id in [
|
||||
"stabilityai/sdxl-turbo",
|
||||
"stabilityai/stable_diffusion-xl-base-1.0",
|
||||
]:
|
||||
if model_ckpt_or_id in default_configs.keys():
|
||||
return model_ckpt_or_id
|
||||
elif "turbo" in model_ckpt_or_id.lower():
|
||||
return "stabilityai/sdxl-turbo"
|
||||
@@ -326,7 +323,7 @@ default_configs = {
|
||||
value="masterpiece, a graceful shark leaping out of the water to catch a fish, eclipsing the sunset, epic, rays of light, silhouette",
|
||||
),
|
||||
gr.Slider(0, 10, value=2),
|
||||
gr.Dropdown(value="EulerAncestralDiscrete"),
|
||||
"EulerAncestralDiscrete",
|
||||
gr.Slider(0, value=0),
|
||||
512,
|
||||
512,
|
||||
|
||||
Reference in New Issue
Block a user