Duplicate from Phr00t/Qwen-Image-Edit-Rapid-AIO

Co-authored-by: Phr00t <[email protected]>

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Qwen-Rapid-AIO.json

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+}

README.md

@@ -0,0 +1,37 @@
+---
+license: apache-2.0
+base_model:
+- Qwen/Qwen-Image-Edit-2509
+- lightx2v/Qwen-Image-Lightning
+pipeline_tag: text-to-image
+library_name: comfyUI
+tags:
+- qwen
+- qwen-edit
+- t2i
+- i2i
+---
+
+Merge of accelerators, VAE and CLIP to allow for easy and fast Qwen Image Edit (and text to image) support.
+
+Use a "Load Checkpoint" node. 1 CFG, 4 step. Use the "TextEncodeQwenImageEditPlus" node for input images (which are optional) and prompt. Provide no images to just do pure text to image. FP8 precision.
+
+**Both NSFW and SFW models are available!** v4 and older combine both NSFW and SFW uses in one model, but performance is subpar. v5+ separates out a NSFW and SFW version, so please pick which model for your use case. 
+
+**Having problems with scaling, cropping or zooming?** Scaling images in the TextEncoderQwenEditPlus node is the problem. There are many workarounds, but I prefer just fixing the node and I've supplied my version in the Files area. It also supports up to 4 input images. Just set the "target_size" to a little less than your output's largest size (like 896 if making a 1024x1024 image). I find this improves quality over skipping scaling entirely, as input images better match output resolutions.
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/631be8402ea8535ea48abbc6/ynDNK35eRLlUjha75fYHH.png)
+
+**V1:** Uses Qwen-Image-Edit-2509 & 4-step Lightning v2.0. Includes a touch of NSFW LORAs, so it should be a very versatile model for both SFW and NSFW use. sa_solver/beta recommended, but euler_a/beta and er_sde/beta can give decent results too.
+
+**V2:** Now uses a mix of Qwen-Image-Edit accelerators, mixing both 8 and 4 steps in one. Also significantly tweaked the NSFW LORAs for better all-around SFW and NSFW use. sa_solver/simple strongly recommended.
+
+**V3:** Uses new Qwen-Image-Edit lightning LORAs for much better results. Also significantly adjusted NSFW LORA mix, removing poor ones and increasing quality ones. sa_solver/beta highly recommended.
+
+**V4:** Mix of many Qwen Edit and base Qwen accelerators, which I think gives better results. Added a touch of a skin correction LORA. **4-5 steps: use sa_solver/simple, lcm/beta or euler_a/beta** and **6-8 steps: use lcm/beta or euler_a/beta only**.
+
+**V5:** NSFW and SFW use cases interfered with eachother too much, so I separated them to specialize in their use cases. Updated "snofs" and "qwen4play" NSFW LORAs + Meta4 for v5.2, then added "Qwen Image NSFW Adv." by fok3827 for v5.3. **SFW: lcm/beta or er_sde/beta generally recommended** and **NSFW: lcm/normal recommended**. Prompting "Professional digital photography" helps reduce the plastic look.
+
+**V6:** Attempt at valiantcat/Qwen-Image-Edit-MeiTu and partially chestnutlzj/Edit-R1-Qwen-Image-Edit-2509 as a base model. However, this was a broken merge. It appears using them as LORAs may work better and I need to cook some more to find something useable. Stay on v5 until something newer comes out.
+
+**V7:** valiantcat/Qwen-Image-Edit-MeiTu and chestnutlzj/Edit-R1-Qwen-Image-Edit-2509 included as LORAs. Accelerator and NSFW LORAs tweaks (v7.1 is more NSFW-heavy). This seemed to be working much better. **lcm/sgm_uniform recommended for 4-6 steps, lcm/normal for 7-8 steps**.

fixed-textencode-node/nodes_qwen.py

@@ -0,0 +1,118 @@
+import node_helpers
+import comfy.utils
+import math
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+class TextEncodeQwenImageEdit(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="TextEncodeQwenImageEdit",
+            category="advanced/conditioning",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("prompt", multiline=True, dynamic_prompts=True),
+                io.Vae.Input("vae", optional=True),
+                io.Image.Input("image", optional=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, clip, prompt, vae=None, image=None) -> io.NodeOutput:
+        ref_latent = None
+        if image is None:
+            images = []
+        else:
+            samples = image.movedim(-1, 1)
+            total = int(1024 * 1024)
+
+            scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
+            width = round(samples.shape[3] * scale_by)
+            height = round(samples.shape[2] * scale_by)
+
+            s = comfy.utils.common_upscale(samples, width, height, "area", "disabled")
+            image = s.movedim(1, -1)
+            images = [image[:, :, :, :3]]
+            if vae is not None:
+                ref_latent = vae.encode(image[:, :, :, :3])
+
+        tokens = clip.tokenize(prompt, images=images)
+        conditioning = clip.encode_from_tokens_scheduled(tokens)
+        if ref_latent is not None:
+            conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": [ref_latent]}, append=True)
+        return io.NodeOutput(conditioning)
+
+
+class TextEncodeQwenImageEditPlus(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="TextEncodeQwenImageEditPlus",
+            category="advanced/conditioning",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("prompt", multiline=True, dynamic_prompts=True),
+                io.Vae.Input("vae", optional=True),
+                io.Image.Input("image1", optional=True),
+                io.Image.Input("image2", optional=True),
+                io.Image.Input("image3", optional=True),
+                io.Image.Input("image4", optional=True),
+                io.Int.Input("target_size", optional=True, default=896, min=128, max=2048, step=32),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, clip, prompt, vae=None, image1=None, image2=None, image3=None, image4=None, target_size=896) -> io.NodeOutput:
+        ref_latents = []
+        images = [image1, image2, image3, image4]
+        images_vl = []
+        llama_template = "<|im_start|>system\nDescribe key details of the input image (including any objects, characters, poses, facial features, clothing, setting, textures and style), then explain how the user's text instruction should alter, modify or recreate the image. Generate a new image that meets the user's requirements, which can vary from a small change to a completely new image using inputs as a guide.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
+        image_prompt = ""
+
+        for i, image in enumerate(images):
+            if image is not None:
+                samples = image.movedim(-1, 1)
+                total = int(384 * 384)
+
+                scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
+                width = round(samples.shape[3] * scale_by)
+                height = round(samples.shape[2] * scale_by)
+
+                s = comfy.utils.common_upscale(samples, width, height, "area", "disabled")
+                images_vl.append(s.movedim(1, -1))
+                if vae is not None:
+                    total = int(target_size * target_size)
+                    scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
+                    
+                    height = int(samples.shape[2] * scale_by / 32) * 32
+                    width = int(samples.shape[3] * scale_by / 32) * 32
+                    
+                    s = comfy.utils.common_upscale(samples, width, height, "lanczos", "center")                    
+                    ref_latents.append(vae.encode(s.movedim(1, -1)[:, :, :, :3]))
+
+                image_prompt += "Picture {}: <|vision_start|><|image_pad|><|vision_end|>".format(i + 1)
+
+        tokens = clip.tokenize(image_prompt + prompt, images=images_vl, llama_template=llama_template)
+        conditioning = clip.encode_from_tokens_scheduled(tokens)
+        if len(ref_latents) > 0:
+            conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": ref_latents}, append=True)
+        return io.NodeOutput(conditioning)
+
+class QwenExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            TextEncodeQwenImageEdit,
+            TextEncodeQwenImageEditPlus,
+        ]
+
+
+async def comfy_entrypoint() -> QwenExtension:
+    return QwenExtension()

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