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add qwen edit plus support
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DeepBeepMeep
parent
2cbcb9523e
commit
76f86c43ad
@ -20,7 +20,7 @@ WanGP supports the Wan (and derived models), Hunyuan Video and LTV Video models
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**Follow DeepBeepMeep on Twitter/X to get the Latest News**: https://x.com/deepbeepmeep
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## 🔥 Latest Updates :
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### September 23 2025: WanGP v8.7 - Here Are Two New Contenders in the Vace Arena !
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### September 24 2025: WanGP v8.72 - Here Are ~~Two~~Three New Contenders in the Vace Arena !
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So in today's release you will find two Wannabe Vace that covers each only a subset of Vace features but offers some interesting advantages:
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- **Wan 2.2 Animate**: this model is specialized in *Body Motion* and *Facial Motion transfers*. It does that very well. You can either *Replace* a person in a Video or *Animate* the person of your choice using an existing *Pose Video* (remember *Animate Anyone* ?). By default it will keep the original soundtrack. *Wan 2.2 Animate* seems to be under the hood a derived i2v model and should support the corresponding Loras Accelerators (for instance *FusioniX i2v*). Also as a WanGP exclusivity, you will find support for *Outpainting*.
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@ -29,7 +29,11 @@ In order to use Wan 2.2 Animate you will need first to stop by the *Mat Anyone*
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- **Lucy Edit**: this one claims to be a *Nano Banana* for Videos. Give it a video and asks it to change it (it is specialized in clothes changing) and voila ! The nice thing about it is that is it based on the *Wan 2.2 5B* model and therefore is very fast especially if you the *FastWan* finetune that is also part of the package.
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Also because I wanted to spoil you:
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- **Qwen Edit Plus**: also known as the *Qwen Edit 25th September Update* which is specialized in combining multiple Objects / People. There is also a new support for *Pose transfer* & *Recolorisation*. All of this made easy to use in WanGP. You will find right now only the quantized version since HF crashes when uploading the unquantized version.
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*Update 8.71*: fixed Fast Lucy Edit that didnt contain the lora
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*Update 8.72*: shadow drop of Qwen Edit Plus
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### September 15 2025: WanGP v8.6 - Attack of the Clones
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17
defaults/qwen_image_edit_plus_20B.json
Normal file
17
defaults/qwen_image_edit_plus_20B.json
Normal file
@ -0,0 +1,17 @@
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{
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"model": {
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"name": "Qwen Image Edit Plus 20B",
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"architecture": "qwen_image_edit_plus_20B",
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"description": "Qwen Image Edit Plus is a generative model that can generate very high quality images with long texts in it. Best results will be at 720p. This model is optimized to combine multiple Subjects & Objects.",
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"URLs": [
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"https://huggingface.co/DeepBeepMeep/Qwen_image/resolve/main/qwen_image_edit_plus_20B_quanto_bf16_int8.safetensors"
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],
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"preload_URLs": "qwen_image_edit_20B",
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"attention": {
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"<89": "sdpa"
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}
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},
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"prompt": "add a hat",
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"resolution": "1024x1024",
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"batch_size": 1
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}
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@ -28,7 +28,7 @@ class family_handler():
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extra_model_def["any_image_refs_relative_size"] = True
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extra_model_def["no_background_removal"] = True
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extra_model_def["image_ref_choices"] = {
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"choices":[("No Reference Image", ""),("First Image is a Reference Image, and then the next ones (up to two) are Style Images", "KI"),
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"choices":[("First Image is a Reference Image, and then the next ones (up to two) are Style Images", "KI"),
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("Up to two Images are Style Images", "KIJ")],
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"default": "KI",
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"letters_filter": "KIJ",
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@ -200,7 +200,8 @@ class QwenImagePipeline(): #DiffusionPipeline
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self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2)
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self.tokenizer_max_length = 1024
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if processor is not None:
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self.prompt_template_encode = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"
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# self.prompt_template_encode = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"
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self.prompt_template_encode = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
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self.prompt_template_encode_start_idx = 64
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else:
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self.prompt_template_encode = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
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@ -232,6 +233,21 @@ class QwenImagePipeline(): #DiffusionPipeline
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txt = [template.format(e) for e in prompt]
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if self.processor is not None and image is not None:
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img_prompt_template = "Picture {}: <|vision_start|><|image_pad|><|vision_end|>"
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if isinstance(image, list):
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base_img_prompt = ""
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for i, img in enumerate(image):
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base_img_prompt += img_prompt_template.format(i + 1)
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elif image is not None:
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base_img_prompt = img_prompt_template.format(1)
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else:
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base_img_prompt = ""
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template = self.prompt_template_encode
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drop_idx = self.prompt_template_encode_start_idx
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txt = [template.format(base_img_prompt + e) for e in prompt]
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model_inputs = self.processor(
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text=txt,
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images=image,
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@ -464,7 +480,7 @@ class QwenImagePipeline(): #DiffusionPipeline
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def prepare_latents(
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self,
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image,
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images,
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batch_size,
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num_channels_latents,
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height,
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@ -482,24 +498,30 @@ class QwenImagePipeline(): #DiffusionPipeline
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shape = (batch_size, num_channels_latents, 1, height, width)
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image_latents = None
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if image is not None:
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image = image.to(device=device, dtype=dtype)
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if image.shape[1] != self.latent_channels:
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image_latents = self._encode_vae_image(image=image, generator=generator)
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else:
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image_latents = image
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if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
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# expand init_latents for batch_size
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additional_image_per_prompt = batch_size // image_latents.shape[0]
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image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)
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elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:
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raise ValueError(
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f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
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)
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else:
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image_latents = torch.cat([image_latents], dim=0)
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if images is not None and len(images ) > 0:
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if not isinstance(images, list):
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images = [images]
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all_image_latents = []
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for image in images:
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image = image.to(device=device, dtype=dtype)
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if image.shape[1] != self.latent_channels:
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image_latents = self._encode_vae_image(image=image, generator=generator)
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else:
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image_latents = image
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if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
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# expand init_latents for batch_size
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additional_image_per_prompt = batch_size // image_latents.shape[0]
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image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)
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elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:
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raise ValueError(
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f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
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)
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else:
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image_latents = torch.cat([image_latents], dim=0)
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image_latents = self._pack_latents(image_latents)
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image_latents = self._pack_latents(image_latents)
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all_image_latents.append(image_latents)
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image_latents = torch.cat(all_image_latents, dim=1)
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if isinstance(generator, list) and len(generator) != batch_size:
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raise ValueError(
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@ -568,6 +590,7 @@ class QwenImagePipeline(): #DiffusionPipeline
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joint_pass= True,
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lora_inpaint = False,
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outpainting_dims = None,
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qwen_edit_plus = False,
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):
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r"""
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Function invoked when calling the pipeline for generation.
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@ -683,61 +706,54 @@ class QwenImagePipeline(): #DiffusionPipeline
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batch_size = prompt_embeds.shape[0]
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device = "cuda"
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prompt_image = None
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condition_images = []
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vae_image_sizes = []
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vae_images = []
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image_mask_latents = None
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if image is not None and not (isinstance(image, torch.Tensor) and image.size(1) == self.latent_channels):
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image = image[0] if isinstance(image, list) else image
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image_height, image_width = self.image_processor.get_default_height_width(image)
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aspect_ratio = image_width / image_height
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if False :
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_, image_width, image_height = min(
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(abs(aspect_ratio - w / h), w, h) for w, h in PREFERRED_QWENIMAGE_RESOLUTIONS
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)
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image_width = image_width // multiple_of * multiple_of
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image_height = image_height // multiple_of * multiple_of
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ref_height, ref_width = 1568, 672
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ref_size = 1024
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ref_text_encoder_size = 384 if qwen_edit_plus else 1024
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if image is not None:
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if not isinstance(image, list): image = [image]
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if height * width < ref_size * ref_size: ref_size = round(math.sqrt(height * width))
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for ref_no, img in enumerate(image):
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image_width, image_height = img.size
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any_mask = ref_no == 0 and image_mask is not None
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if (image_height * image_width > ref_size * ref_size) and not any_mask:
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vae_height, vae_width =calculate_new_dimensions(ref_size, ref_size, image_height, image_width, False, block_size=multiple_of)
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else:
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vae_height, vae_width = image_height, image_width
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vae_width = vae_width // multiple_of * multiple_of
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vae_height = vae_height // multiple_of * multiple_of
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vae_image_sizes.append((vae_width, vae_height))
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condition_height, condition_width =calculate_new_dimensions(ref_text_encoder_size, ref_text_encoder_size, image_height, image_width, False, block_size=multiple_of)
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condition_images.append(img.resize((condition_width, condition_height), resample=Image.Resampling.LANCZOS) )
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if img.size != (vae_width, vae_height):
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img = img.resize((vae_width, vae_height), resample=Image.Resampling.LANCZOS)
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if any_mask :
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if lora_inpaint:
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image_mask_rebuilt = torch.where(convert_image_to_tensor(image_mask)>-0.5, 1., 0. )[0:1]
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img = convert_image_to_tensor(img)
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green = torch.tensor([-1.0, 1.0, -1.0]).to(img)
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green_image = green[:, None, None] .expand_as(img)
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img = torch.where(image_mask_rebuilt > 0, green_image, img)
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img = convert_tensor_to_image(img)
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else:
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image_mask_latents = convert_image_to_tensor(image_mask.resize((vae_width // 8, vae_height // 8), resample=Image.Resampling.LANCZOS))
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image_mask_latents = torch.where(image_mask_latents>-0.5, 1., 0. )[0:1]
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image_mask_rebuilt = image_mask_latents.repeat_interleave(8, dim=-1).repeat_interleave(8, dim=-2).unsqueeze(0)
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# convert_tensor_to_image( image_mask_rebuilt.squeeze(0).repeat(3,1,1)).save("mmm.png")
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image_mask_latents = image_mask_latents.to(device).unsqueeze(0).unsqueeze(0).repeat(1,16,1,1,1)
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image_mask_latents = self._pack_latents(image_mask_latents)
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# img.save("nnn.png")
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vae_images.append( convert_image_to_tensor(img).unsqueeze(0).unsqueeze(2) )
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if image_mask is None:
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if height * width < ref_height * ref_width: ref_height , ref_width = height , width
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if image_height * image_width > ref_height * ref_width:
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image_height, image_width = calculate_new_dimensions(ref_height, ref_width, image_height, image_width, False, block_size=multiple_of)
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if (image_width,image_height) != image.size:
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image = image.resize((image_width,image_height), resample=Image.Resampling.LANCZOS)
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elif not lora_inpaint:
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# _, image_width, image_height = min(
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# (abs(aspect_ratio - w / h), w, h) for w, h in PREFERRED_QWENIMAGE_RESOLUTIONS
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# )
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image_height, image_width = calculate_new_dimensions(height, width, image_height, image_width, False, block_size=multiple_of)
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# image_height, image_width = calculate_new_dimensions(ref_height, ref_width, image_height, image_width, False, block_size=multiple_of)
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height, width = image_height, image_width
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image_mask_latents = convert_image_to_tensor(image_mask.resize((width // 8, height // 8), resample=Image.Resampling.LANCZOS))
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image_mask_latents = torch.where(image_mask_latents>-0.5, 1., 0. )[0:1]
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image_mask_rebuilt = image_mask_latents.repeat_interleave(8, dim=-1).repeat_interleave(8, dim=-2).unsqueeze(0)
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# convert_tensor_to_image( image_mask_rebuilt.squeeze(0).repeat(3,1,1)).save("mmm.png")
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image_mask_latents = image_mask_latents.to(device).unsqueeze(0).unsqueeze(0).repeat(1,16,1,1,1)
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image_mask_latents = self._pack_latents(image_mask_latents)
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prompt_image = image
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if image.size != (image_width, image_height):
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image = image.resize((image_width, image_height), resample=Image.Resampling.LANCZOS)
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image = convert_image_to_tensor(image)
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if lora_inpaint:
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image_mask_rebuilt = torch.where(convert_image_to_tensor(image_mask)>-0.5, 1., 0. )[0:1]
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image_mask_latents = None
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green = torch.tensor([-1.0, 1.0, -1.0]).to(image)
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green_image = green[:, None, None] .expand_as(image)
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image = torch.where(image_mask_rebuilt > 0, green_image, image)
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prompt_image = convert_tensor_to_image(image)
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image = image.unsqueeze(0).unsqueeze(2)
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# image.save("nnn.png")
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has_neg_prompt = negative_prompt is not None or (
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negative_prompt_embeds is not None and negative_prompt_embeds_mask is not None
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)
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do_true_cfg = true_cfg_scale > 1 and has_neg_prompt
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prompt_embeds, prompt_embeds_mask = self.encode_prompt(
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image=prompt_image,
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image=condition_images,
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prompt=prompt,
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prompt_embeds=prompt_embeds,
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prompt_embeds_mask=prompt_embeds_mask,
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@ -747,7 +763,7 @@ class QwenImagePipeline(): #DiffusionPipeline
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)
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if do_true_cfg:
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negative_prompt_embeds, negative_prompt_embeds_mask = self.encode_prompt(
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image=prompt_image,
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image=condition_images,
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prompt=negative_prompt,
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prompt_embeds=negative_prompt_embeds,
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prompt_embeds_mask=negative_prompt_embeds_mask,
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@ -763,7 +779,7 @@ class QwenImagePipeline(): #DiffusionPipeline
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# 4. Prepare latent variables
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num_channels_latents = self.transformer.in_channels // 4
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latents, image_latents = self.prepare_latents(
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image,
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vae_images,
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batch_size * num_images_per_prompt,
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num_channels_latents,
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height,
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@ -779,7 +795,12 @@ class QwenImagePipeline(): #DiffusionPipeline
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img_shapes = [
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[
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(1, height // self.vae_scale_factor // 2, width // self.vae_scale_factor // 2),
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(1, image_height // self.vae_scale_factor // 2, image_width // self.vae_scale_factor // 2),
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# (1, image_height // self.vae_scale_factor // 2, image_width // self.vae_scale_factor // 2),
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*[
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(1, vae_height // self.vae_scale_factor // 2, vae_width // self.vae_scale_factor // 2)
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for vae_width, vae_height in vae_image_sizes
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],
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]
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] * batch_size
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else:
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@ -971,7 +992,7 @@ class QwenImagePipeline(): #DiffusionPipeline
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latents = latents / latents_std + latents_mean
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output_image = self.vae.decode(latents, return_dict=False)[0][:, :, 0]
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if image_mask is not None and not lora_inpaint : #not (lora_inpaint and outpainting_dims is not None):
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output_image = image.squeeze(2) * (1 - image_mask_rebuilt) + output_image.to(image) * image_mask_rebuilt
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output_image = vae_images[0].squeeze(2) * (1 - image_mask_rebuilt) + output_image.to(vae_images[0] ) * image_mask_rebuilt
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return output_image
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@ -20,7 +20,7 @@ class family_handler():
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"fit_into_canvas_image_refs": 0,
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}
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if base_model_type in ["qwen_image_edit_20B"]:
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if base_model_type in ["qwen_image_edit_20B", "qwen_image_edit_plus_20B"]:
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extra_model_def["inpaint_support"] = True
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extra_model_def["image_ref_choices"] = {
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"choices": [
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@ -42,11 +42,20 @@ class family_handler():
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"image_modes" : [2],
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}
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if base_model_type in ["qwen_image_edit_plus_20B"]:
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extra_model_def["guide_preprocessing"] = {
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"selection": ["", "PV", "SV", "CV"],
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}
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extra_model_def["mask_preprocessing"] = {
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"selection": ["", "A"],
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"visible": False,
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}
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return extra_model_def
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@staticmethod
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def query_supported_types():
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return ["qwen_image_20B", "qwen_image_edit_20B"]
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return ["qwen_image_20B", "qwen_image_edit_20B", "qwen_image_edit_plus_20B"]
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@staticmethod
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def query_family_maps():
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@ -113,9 +122,15 @@ class family_handler():
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"denoising_strength" : 1.,
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"model_mode" : 0,
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})
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elif base_model_type in ["qwen_image_edit_plus_20B"]:
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ui_defaults.update({
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"video_prompt_type": "I",
|
||||
"denoising_strength" : 1.,
|
||||
"model_mode" : 0,
|
||||
})
|
||||
|
||||
def validate_generative_settings(base_model_type, model_def, inputs):
|
||||
if base_model_type in ["qwen_image_edit_20B"]:
|
||||
if base_model_type in ["qwen_image_edit_20B", "qwen_image_edit_plus_20B"]:
|
||||
model_mode = inputs["model_mode"]
|
||||
denoising_strength= inputs["denoising_strength"]
|
||||
video_guide_outpainting= inputs["video_guide_outpainting"]
|
||||
|
||||
@ -51,10 +51,10 @@ class model_factory():
|
||||
transformer_filename = model_filename[0]
|
||||
processor = None
|
||||
tokenizer = None
|
||||
if base_model_type == "qwen_image_edit_20B":
|
||||
if base_model_type in ["qwen_image_edit_20B", "qwen_image_edit_plus_20B"]:
|
||||
processor = Qwen2VLProcessor.from_pretrained(os.path.join(checkpoint_dir,"Qwen2.5-VL-7B-Instruct"))
|
||||
tokenizer = AutoTokenizer.from_pretrained(os.path.join(checkpoint_dir,"Qwen2.5-VL-7B-Instruct"))
|
||||
|
||||
self.base_model_type = base_model_type
|
||||
|
||||
base_config_file = "configs/qwen_image_20B.json"
|
||||
with open(base_config_file, 'r', encoding='utf-8') as f:
|
||||
@ -173,7 +173,7 @@ class model_factory():
|
||||
self.vae.tile_latent_min_height = VAE_tile_size[1]
|
||||
self.vae.tile_latent_min_width = VAE_tile_size[1]
|
||||
|
||||
|
||||
qwen_edit_plus = self.base_model_type in ["qwen_image_edit_plus_20B"]
|
||||
self.vae.enable_slicing()
|
||||
# width, height = aspect_ratios["16:9"]
|
||||
|
||||
@ -182,17 +182,19 @@ class model_factory():
|
||||
|
||||
image_mask = None if input_masks is None else convert_tensor_to_image(input_masks, mask_levels= True)
|
||||
if input_frames is not None:
|
||||
input_ref_images = [convert_tensor_to_image(input_frames) ]
|
||||
elif input_ref_images is not None:
|
||||
input_ref_images = [convert_tensor_to_image(input_frames) ] + ([] if input_ref_images is None else input_ref_images )
|
||||
|
||||
if input_ref_images is not None:
|
||||
# image stiching method
|
||||
stiched = input_ref_images[0]
|
||||
if "K" in video_prompt_type :
|
||||
w, h = input_ref_images[0].size
|
||||
height, width = calculate_new_dimensions(height, width, h, w, fit_into_canvas)
|
||||
|
||||
for new_img in input_ref_images[1:]:
|
||||
stiched = stitch_images(stiched, new_img)
|
||||
input_ref_images = [stiched]
|
||||
if not qwen_edit_plus:
|
||||
for new_img in input_ref_images[1:]:
|
||||
stiched = stitch_images(stiched, new_img)
|
||||
input_ref_images = [stiched]
|
||||
|
||||
image = self.pipeline(
|
||||
prompt=input_prompt,
|
||||
@ -212,7 +214,8 @@ class model_factory():
|
||||
generator=torch.Generator(device="cuda").manual_seed(seed),
|
||||
lora_inpaint = image_mask is not None and model_mode == 1,
|
||||
outpainting_dims = outpainting_dims,
|
||||
)
|
||||
qwen_edit_plus = qwen_edit_plus,
|
||||
)
|
||||
if image is None: return None
|
||||
return image.transpose(0, 1)
|
||||
|
||||
|
||||
@ -21,6 +21,7 @@ from .utils.get_default_model import get_matanyone_model
|
||||
from .matanyone.inference.inference_core import InferenceCore
|
||||
from .matanyone_wrapper import matanyone
|
||||
from shared.utils.audio_video import save_video, save_image
|
||||
from mmgp import offload
|
||||
|
||||
arg_device = "cuda"
|
||||
arg_sam_model_type="vit_h"
|
||||
@ -539,7 +540,7 @@ def video_matting(video_state,video_input, end_slider, matting_type, interactive
|
||||
file_name = ".".join(file_name.split(".")[:-1])
|
||||
|
||||
from shared.utils.audio_video import extract_audio_tracks, combine_video_with_audio_tracks, cleanup_temp_audio_files
|
||||
source_audio_tracks, audio_metadata = extract_audio_tracks(video_input)
|
||||
source_audio_tracks, audio_metadata = extract_audio_tracks(video_input, verbose= offload.default_verboseLevel )
|
||||
output_fg_path = f"./mask_outputs/{file_name}_fg.mp4"
|
||||
output_fg_temp_path = f"./mask_outputs/{file_name}_fg_tmp.mp4"
|
||||
if len(source_audio_tracks) == 0:
|
||||
@ -679,7 +680,6 @@ def load_unload_models(selected):
|
||||
}
|
||||
# os.path.join('.')
|
||||
|
||||
from mmgp import offload
|
||||
|
||||
# sam_checkpoint = load_file_from_url(sam_checkpoint_url_dict[arg_sam_model_type], ".")
|
||||
sam_checkpoint = None
|
||||
|
||||
@ -321,7 +321,7 @@ def fit_image_into_canvas(ref_img, image_size, canvas_tf_bg =127.5, device ="cpu
|
||||
ref_width, ref_height = ref_img.size
|
||||
if (ref_height, ref_width) == image_size and outpainting_dims == None:
|
||||
ref_img = TF.to_tensor(ref_img).sub_(0.5).div_(0.5).unsqueeze(1)
|
||||
canvas = torch.zeros_like(ref_img) if return_mask else None
|
||||
canvas = torch.zeros_like(ref_img[:1]) if return_mask else None
|
||||
else:
|
||||
if outpainting_dims != None:
|
||||
final_height, final_width = image_size
|
||||
@ -374,7 +374,7 @@ def prepare_video_guide_and_mask( video_guides, video_masks, pre_video_guide, im
|
||||
if pre_video_guide is not None:
|
||||
src_video = pre_video_guide if src_video is None else torch.cat( [pre_video_guide, src_video], dim=1)
|
||||
if any_mask:
|
||||
src_mask = torch.zeros_like(pre_video_guide[0:1]) if src_mask is None else torch.cat( [torch.zeros_like(pre_video_guide[0:1]), src_mask], dim=1)
|
||||
src_mask = torch.zeros_like(pre_video_guide[:1]) if src_mask is None else torch.cat( [torch.zeros_like(pre_video_guide[:1]), src_mask], dim=1)
|
||||
|
||||
if any_guide_padding:
|
||||
if src_video is None:
|
||||
|
||||
35
wgp.py
35
wgp.py
@ -63,7 +63,7 @@ AUTOSAVE_FILENAME = "queue.zip"
|
||||
PROMPT_VARS_MAX = 10
|
||||
|
||||
target_mmgp_version = "3.6.0"
|
||||
WanGP_version = "8.71"
|
||||
WanGP_version = "8.72"
|
||||
settings_version = 2.35
|
||||
max_source_video_frames = 3000
|
||||
prompt_enhancer_image_caption_model, prompt_enhancer_image_caption_processor, prompt_enhancer_llm_model, prompt_enhancer_llm_tokenizer = None, None, None, None
|
||||
@ -4987,7 +4987,7 @@ def generate_video(
|
||||
frames_to_inject[pos] = image_refs[i]
|
||||
|
||||
|
||||
video_guide_processed = video_mask_processed = video_guide_processed2 = video_mask_processed2 = None
|
||||
video_guide_processed = video_mask_processed = video_guide_processed2 = video_mask_processed2 = sparse_video_image = None
|
||||
if video_guide is not None:
|
||||
keep_frames_parsed_full, error = parse_keep_frames_video_guide(keep_frames_video_guide, source_video_frames_count -source_video_overlap_frames_count + requested_frames_to_generate)
|
||||
if len(error) > 0:
|
||||
@ -6566,11 +6566,11 @@ def switch_image_mode(state):
|
||||
inpaint_support = model_def.get("inpaint_support", False)
|
||||
if inpaint_support:
|
||||
if image_mode == 1:
|
||||
video_prompt_type = del_in_sequence(video_prompt_type, "VAG")
|
||||
video_prompt_type = del_in_sequence(video_prompt_type, "VAG" + all_guide_processes)
|
||||
video_prompt_type = add_to_sequence(video_prompt_type, "KI")
|
||||
elif image_mode == 2:
|
||||
video_prompt_type = del_in_sequence(video_prompt_type, "KI" + all_guide_processes)
|
||||
video_prompt_type = add_to_sequence(video_prompt_type, "VAG")
|
||||
video_prompt_type = del_in_sequence(video_prompt_type, "KI")
|
||||
ui_defaults["video_prompt_type"] = video_prompt_type
|
||||
|
||||
return str(time.time())
|
||||
@ -6965,10 +6965,11 @@ def refresh_video_prompt_type_alignment(state, video_prompt_type, video_prompt_t
|
||||
video_prompt_type = add_to_sequence(video_prompt_type, video_prompt_type_video_guide)
|
||||
return video_prompt_type
|
||||
|
||||
all_guide_processes ="PDESLCMUVB"
|
||||
|
||||
def refresh_video_prompt_type_video_guide(state, video_prompt_type, video_prompt_type_video_guide, image_mode, old_image_mask_guide_value, old_image_guide_value, old_image_mask_value ):
|
||||
old_video_prompt_type = video_prompt_type
|
||||
video_prompt_type = del_in_sequence(video_prompt_type, "PDESLCMUVB")
|
||||
video_prompt_type = del_in_sequence(video_prompt_type, all_guide_processes)
|
||||
video_prompt_type = add_to_sequence(video_prompt_type, video_prompt_type_video_guide)
|
||||
visible = "V" in video_prompt_type
|
||||
model_type = state["model_type"]
|
||||
@ -6978,8 +6979,12 @@ def refresh_video_prompt_type_video_guide(state, video_prompt_type, video_prompt
|
||||
image_outputs = image_mode > 0
|
||||
keep_frames_video_guide_visible = not image_outputs and visible and not model_def.get("keep_frames_video_guide_not_supported", False)
|
||||
image_mask_guide, image_guide, image_mask = switch_image_guide_editor(image_mode, old_video_prompt_type , video_prompt_type, old_image_mask_guide_value, old_image_guide_value, old_image_mask_value )
|
||||
|
||||
return video_prompt_type, gr.update(visible = visible and not image_outputs), image_guide, gr.update(visible = keep_frames_video_guide_visible), gr.update(visible = visible and "G" in video_prompt_type), gr.update(visible= (visible or "F" in video_prompt_type or "K" in video_prompt_type) and any_outpainting), gr.update(visible= visible and not "U" in video_prompt_type ), gr.update(visible= mask_visible and not image_outputs), image_mask, image_mask_guide, gr.update(visible= mask_visible)
|
||||
mask_preprocessing = model_def.get("mask_preprocessing", None)
|
||||
if mask_preprocessing is not None:
|
||||
mask_selector_visible = mask_preprocessing.get("visible", True)
|
||||
else:
|
||||
mask_selector_visible = True
|
||||
return video_prompt_type, gr.update(visible = visible and not image_outputs), image_guide, gr.update(visible = keep_frames_video_guide_visible), gr.update(visible = visible and "G" in video_prompt_type), gr.update(visible= (visible or "F" in video_prompt_type or "K" in video_prompt_type) and any_outpainting), gr.update(visible= visible and mask_selector_visible and not "U" in video_prompt_type ) , gr.update(visible= mask_visible and not image_outputs), image_mask, image_mask_guide, gr.update(visible= mask_visible)
|
||||
|
||||
|
||||
def refresh_video_prompt_type_video_guide_alt(state, video_prompt_type, video_prompt_type_video_guide_alt, image_mode):
|
||||
@ -7391,7 +7396,7 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
|
||||
any_start_image = any_end_image = any_reference_image = any_image_mask = False
|
||||
v2i_switch_supported = (vace or t2v or standin) and not image_outputs
|
||||
ti2v_2_2 = base_model_type in ["ti2v_2_2"]
|
||||
gallery_height = 350
|
||||
gallery_height = 550
|
||||
def get_image_gallery(label ="", value = None, single_image_mode = False, visible = False ):
|
||||
with gr.Row(visible = visible) as gallery_row:
|
||||
gallery_amg = AdvancedMediaGallery(media_mode="image", height=gallery_height, columns=4, label=label, initial = value , single_image_mode = single_image_mode )
|
||||
@ -7459,8 +7464,12 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
|
||||
keep_frames_video_source = gr.Text(value=ui_defaults.get("keep_frames_video_source","") , visible= len(filter_letters(image_prompt_type_value, "VL"))>0 , scale = 2, label= "Truncate Video beyond this number of resampled Frames (empty=Keep All, negative truncates from End)" )
|
||||
|
||||
any_control_video = any_control_image = False
|
||||
guide_preprocessing = model_def.get("guide_preprocessing", None)
|
||||
mask_preprocessing = model_def.get("mask_preprocessing", None)
|
||||
if image_mode_value ==2:
|
||||
guide_preprocessing = { "selection": ["V", "VG"]}
|
||||
mask_preprocessing = { "selection": ["A"]}
|
||||
else:
|
||||
guide_preprocessing = model_def.get("guide_preprocessing", None)
|
||||
mask_preprocessing = model_def.get("mask_preprocessing", None)
|
||||
guide_custom_choices = model_def.get("guide_custom_choices", None)
|
||||
image_ref_choices = model_def.get("image_ref_choices", None)
|
||||
|
||||
@ -7504,7 +7513,7 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
|
||||
if image_outputs: video_prompt_type_video_guide_label = video_prompt_type_video_guide_label.replace("Video", "Image")
|
||||
video_prompt_type_video_guide = gr.Dropdown(
|
||||
guide_preprocessing_choices,
|
||||
value=filter_letters(video_prompt_type_value, "PDESLCMUVB", guide_preprocessing.get("default", "") ),
|
||||
value=filter_letters(video_prompt_type_value, all_guide_processes, guide_preprocessing.get("default", "") ),
|
||||
label= video_prompt_type_video_guide_label , scale = 2, visible= guide_preprocessing.get("visible", True) , show_label= True,
|
||||
)
|
||||
any_control_video = True
|
||||
@ -7590,8 +7599,8 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
|
||||
if image_guide_value is None:
|
||||
image_mask_guide_value = None
|
||||
else:
|
||||
image_mask_value = rgb_bw_to_rgba_mask(image_mask_value)
|
||||
image_mask_guide_value = { "background" : image_guide_value, "composite" : None, "layers": [image_mask_value] }
|
||||
image_mask_guide_value = { "background" : image_guide_value, "composite" : None}
|
||||
image_mask_guide_value["layers"] = [] if image_mask_value is None else [rgb_bw_to_rgba_mask(image_mask_value)]
|
||||
|
||||
image_mask_guide = gr.ImageEditor(
|
||||
label="Control Image to be Inpainted" if image_mode_value == 2 else "Control Image and Mask",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user