Spaces:
Running
on
Zero
Running
on
Zero
update demo
Browse files- app.py +338 -4
- requirements.txt +7 -0
app.py
CHANGED
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@@ -1,7 +1,341 @@
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import gradio as gr
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import gradio as gr
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import numpy as np
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import random
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import torch
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import spaces
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from PIL import Image
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from diffusers import QwenImageEditPlusPipeline
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import os
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import base64
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import json
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SYSTEM_PROMPT = '''
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+
# Edit Instruction Rewriter
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You are a professional edit instruction rewriter. Your task is to generate a precise, concise, and visually achievable professional-level edit instruction based on the user-provided instruction and the image to be edited.
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Please strictly follow the rewriting rules below:
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+
## 1. General Principles
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- Keep the rewritten prompt **concise and comprehensive**. Avoid overly long sentences and unnecessary descriptive language.
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- If the instruction is contradictory, vague, or unachievable, prioritize reasonable inference and correction, and supplement details when necessary.
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- Keep the main part of the original instruction unchanged, only enhancing its clarity, rationality, and visual feasibility.
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- All added objects or modifications must align with the logic and style of the scene in the input images.
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- If multiple sub-images are to be generated, describe the content of each sub-image individually.
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## 2. Task-Type Handling Rules
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### 1. Add, Delete, Replace Tasks
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- If the instruction is clear (already includes task type, target entity, position, quantity, attributes), preserve the original intent and only refine the grammar.
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- If the description is vague, supplement with minimal but sufficient details (category, color, size, orientation, position, etc.). For example:
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> Original: "Add an animal"
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> Rewritten: "Add a light-gray cat in the bottom-right corner, sitting and facing the camera"
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- Remove meaningless instructions: e.g., "Add 0 objects" should be ignored or flagged as invalid.
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- For replacement tasks, specify "Replace Y with X" and briefly describe the key visual features of X.
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### 2. Text Editing Tasks
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- All text content must be enclosed in English double quotes `" "`. Keep the original language of the text, and keep the capitalization.
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- Both adding new text and replacing existing text are text replacement tasks, For example:
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- Replace "xx" to "yy"
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- Replace the mask / bounding box to "yy"
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- Replace the visual object to "yy"
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- Specify text position, color, and layout only if user has required.
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- If font is specified, keep the original language of the font.
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### 3. Human Editing Tasks
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- Make the smallest changes to the given user's prompt.
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- If changes to background, action, expression, camera shot, or ambient lighting are required, please list each modification individually.
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- **Edits to makeup or facial features / expression must be subtle, not exaggerated, and must preserve the subject’s identity consistency.**
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> Original: "Add eyebrows to the face"
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> Rewritten: "Slightly thicken the person’s eyebrows with little change, look natural."
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### 4. Style Conversion or Enhancement Tasks
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- If a style is specified, describe it concisely using key visual features. For example:
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> Original: "Disco style"
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> Rewritten: "1970s disco style: flashing lights, disco ball, mirrored walls, vibrant colors"
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- For style reference, analyze the original image and extract key characteristics (color, composition, texture, lighting, artistic style, etc.), integrating them into the instruction.
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- **Colorization tasks (including old photo restoration) must use the fixed template:**
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"Restore and colorize the old photo."
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- Clearly specify the object to be modified. For example:
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> Original: Modify the subject in Picture 1 to match the style of Picture 2.
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> Rewritten: Change the girl in Picture 1 to the ink-wash style of Picture 2 — rendered in black-and-white watercolor with soft color transitions.
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### 5. Material Replacement
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- Clearly specify the object and the material. For example: "Change the material of the apple to papercut style."
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- For text material replacement, use the fixed template:
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"Change the material of text "xxxx" to laser style"
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### 6. Logo/Pattern Editing
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- Material replacement should preserve the original shape and structure as much as possible. For example:
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> Original: "Convert to sapphire material"
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> Rewritten: "Convert the main subject in the image to sapphire material, preserving similar shape and structure"
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- When migrating logos/patterns to new scenes, ensure shape and structure consistency. For example:
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> Original: "Migrate the logo in the image to a new scene"
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> Rewritten: "Migrate the logo in the image to a new scene, preserving similar shape and structure"
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### 7. Multi-Image Tasks
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- Rewritten prompts must clearly point out which image’s element is being modified. For example:
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> Original: "Replace the subject of picture 1 with the subject of picture 2"
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> Rewritten: "Replace the girl of picture 1 with the boy of picture 2, keeping picture 2’s background unchanged"
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- For stylization tasks, describe the reference image’s style in the rewritten prompt, while preserving the visual content of the source image.
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## 3. Rationale and Logic Check
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- Resolve contradictory instructions: e.g., “Remove all trees but keep all trees” requires logical correction.
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- Supplement missing critical information: e.g., if position is unspecified, choose a reasonable area based on composition (near subject, blank space, center/edge, etc.).
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# Output Format Example
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```json
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{
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"Rewritten": "..."
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}
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'''
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def polish_prompt(prompt, img):
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prompt = f"{SYSTEM_PROMPT}\n\nUser Input: {prompt}\n\nRewritten Prompt:"
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success=False
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while not success:
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try:
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result = api(prompt, [img])
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# print(f"Result: {result}")
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# print(f"Polished Prompt: {polished_prompt}")
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if isinstance(result, str):
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result = result.replace('```json','')
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result = result.replace('```','')
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result = json.loads(result)
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else:
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result = json.loads(result)
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polished_prompt = result['Rewritten']
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polished_prompt = polished_prompt.strip()
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polished_prompt = polished_prompt.replace("\n", " ")
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success = True
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except Exception as e:
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print(f"[Warning] Error during API call: {e}")
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return polished_prompt
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def encode_image(pil_image):
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import io
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buffered = io.BytesIO()
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pil_image.save(buffered, format="PNG")
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return base64.b64encode(buffered.getvalue()).decode("utf-8")
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def api(prompt, img_list, model="qwen-vl-max-latest", kwargs={}):
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import dashscope
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api_key = os.environ.get('DASH_API_KEY')
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if not api_key:
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raise EnvironmentError("DASH_API_KEY is not set")
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assert model in ["qwen-vl-max-latest"], f"Not implemented model {model}"
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sys_promot = "you are a helpful assistant, you should provide useful answers to users."
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messages = [
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{"role": "system", "content": sys_promot},
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{"role": "user", "content": []}]
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for img in img_list:
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messages[1]["content"].append(
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{"image": f"data:image/png;base64,{encode_image(img)}"})
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messages[1]["content"].append({"text": f"{prompt}"})
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response_format = kwargs.get('response_format', None)
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response = dashscope.MultiModalConversation.call(
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api_key=api_key,
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model=model, # For example, use qwen-plus here. You can change the model name as needed. Model list: https://help.aliyun.com/zh/model-studio/getting-started/models
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messages=messages,
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result_format='message',
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response_format=response_format,
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)
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if response.status_code == 200:
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return response.output.choices[0].message.content[0]['text']
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else:
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raise Exception(f'Failed to post: {response}')
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# --- Model Loading ---
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dtype = torch.bfloat16
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# Load the model pipeline
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pipe = QwenImageEditPlusPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2509", torch_dtype=dtype).to(device)
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# --- UI Constants and Helpers ---
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MAX_SEED = np.iinfo(np.int32).max
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# --- Main Inference Function (with hardcoded negative prompt) ---
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@spaces.GPU(duration=300)
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def infer(
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images,
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prompt,
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seed=42,
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randomize_seed=False,
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true_guidance_scale=1.0,
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num_inference_steps=50,
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height=None,
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width=None,
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rewrite_prompt=True,
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num_images_per_prompt=1,
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progress=gr.Progress(track_tqdm=True),
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):
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"""
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Generates an image using the local Qwen-Image diffusers pipeline.
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"""
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# Hardcode the negative prompt as requested
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negative_prompt = " "
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| 188 |
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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# Set up the generator for reproducibility
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generator = torch.Generator(device=device).manual_seed(seed)
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| 194 |
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# Load input images into PIL Images
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pil_images = []
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| 196 |
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if images is not None:
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for item in images:
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try:
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if isinstance(item[0], Image.Image):
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pil_images.append(item[0].convert("RGB"))
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elif isinstance(item[0], str):
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pil_images.append(Image.open(item[0]).convert("RGB"))
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| 203 |
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elif hasattr(item, "name"):
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pil_images.append(Image.open(item.name).convert("RGB"))
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except Exception:
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continue
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| 208 |
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if height==256 and width==256:
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height, width = None, None
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print(f"Calling pipeline with prompt: '{prompt}'")
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| 211 |
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print(f"Negative Prompt: '{negative_prompt}'")
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| 212 |
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print(f"Seed: {seed}, Steps: {num_inference_steps}, Guidance: {true_guidance_scale}, Size: {width}x{height}")
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| 213 |
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if rewrite_prompt and len(pil_images) > 0:
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prompt = polish_prompt(prompt, pil_images[0])
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print(f"Rewritten Prompt: {prompt}")
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| 216 |
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| 217 |
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# Generate the image
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image = pipe(
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| 220 |
+
image=pil_images if len(pil_images) > 0 else None,
|
| 221 |
+
prompt=prompt,
|
| 222 |
+
height=height,
|
| 223 |
+
width=width,
|
| 224 |
+
negative_prompt=negative_prompt,
|
| 225 |
+
num_inference_steps=num_inference_steps,
|
| 226 |
+
generator=generator,
|
| 227 |
+
true_cfg_scale=true_guidance_scale,
|
| 228 |
+
num_images_per_prompt=num_images_per_prompt,
|
| 229 |
+
).images
|
| 230 |
+
|
| 231 |
+
return image, seed
|
| 232 |
+
|
| 233 |
+
# --- Examples and UI Layout ---
|
| 234 |
+
examples = []
|
| 235 |
+
|
| 236 |
+
css = """
|
| 237 |
+
#col-container {
|
| 238 |
+
margin: 0 auto;
|
| 239 |
+
max-width: 1024px;
|
| 240 |
+
}
|
| 241 |
+
#edit_text{margin-top: -62px !important}
|
| 242 |
+
"""
|
| 243 |
+
|
| 244 |
+
with gr.Blocks(css=css) as demo:
|
| 245 |
+
with gr.Column(elem_id="col-container"):
|
| 246 |
+
gr.HTML('<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/qwen_image_edit_logo.png" alt="Qwen-Image Logo" width="400" style="display: block; margin: 0 auto;">')
|
| 247 |
+
gr.Markdown("[Learn more](https://github.com/QwenLM/Qwen-Image) about the Qwen-Image series. Try on [Qwen Chat](https://chat.qwen.ai/), or [download model](https://huggingface.co/Qwen/Qwen-Image-Edit) to run locally with ComfyUI or diffusers.")
|
| 248 |
+
with gr.Row():
|
| 249 |
+
with gr.Column():
|
| 250 |
+
input_images = gr.Gallery(label="Input Images", show_label=False, type="pil", interactive=True)
|
| 251 |
+
|
| 252 |
+
# result = gr.Image(label="Result", show_label=False, type="pil")
|
| 253 |
+
result = gr.Gallery(label="Result", show_label=False, type="pil")
|
| 254 |
+
with gr.Row():
|
| 255 |
+
prompt = gr.Text(
|
| 256 |
+
label="Prompt",
|
| 257 |
+
show_label=False,
|
| 258 |
+
placeholder="describe the edit instruction",
|
| 259 |
+
container=False,
|
| 260 |
+
)
|
| 261 |
+
run_button = gr.Button("Edit!", variant="primary")
|
| 262 |
+
|
| 263 |
+
with gr.Accordion("Advanced Settings", open=False):
|
| 264 |
+
# Negative prompt UI element is removed here
|
| 265 |
+
|
| 266 |
+
seed = gr.Slider(
|
| 267 |
+
label="Seed",
|
| 268 |
+
minimum=0,
|
| 269 |
+
maximum=MAX_SEED,
|
| 270 |
+
step=1,
|
| 271 |
+
value=0,
|
| 272 |
+
)
|
| 273 |
+
|
| 274 |
+
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
|
| 275 |
+
|
| 276 |
+
with gr.Row():
|
| 277 |
+
|
| 278 |
+
true_guidance_scale = gr.Slider(
|
| 279 |
+
label="True guidance scale",
|
| 280 |
+
minimum=1.0,
|
| 281 |
+
maximum=10.0,
|
| 282 |
+
step=0.1,
|
| 283 |
+
value=4.0
|
| 284 |
+
)
|
| 285 |
+
|
| 286 |
+
num_inference_steps = gr.Slider(
|
| 287 |
+
label="Number of inference steps",
|
| 288 |
+
minimum=1,
|
| 289 |
+
maximum=50,
|
| 290 |
+
step=1,
|
| 291 |
+
value=40,
|
| 292 |
+
)
|
| 293 |
+
|
| 294 |
+
height = gr.Slider(
|
| 295 |
+
label="Height",
|
| 296 |
+
minimum=256,
|
| 297 |
+
maximum=2048,
|
| 298 |
+
step=8,
|
| 299 |
+
value=None,
|
| 300 |
+
)
|
| 301 |
+
|
| 302 |
+
width = gr.Slider(
|
| 303 |
+
label="Width",
|
| 304 |
+
minimum=256,
|
| 305 |
+
maximum=2048,
|
| 306 |
+
step=8,
|
| 307 |
+
value=None,
|
| 308 |
+
)
|
| 309 |
+
|
| 310 |
+
num_images_per_prompt = gr.Slider(
|
| 311 |
+
label="Number of images per prompt",
|
| 312 |
+
minimum=1,
|
| 313 |
+
maximum=4,
|
| 314 |
+
step=1,
|
| 315 |
+
value=1,
|
| 316 |
+
)
|
| 317 |
+
|
| 318 |
+
rewrite_prompt = gr.Checkbox(label="Rewrite prompt", value=False)
|
| 319 |
+
|
| 320 |
+
# gr.Examples(examples=examples, inputs=[prompt], outputs=[result, seed], fn=infer, cache_examples=False)
|
| 321 |
+
|
| 322 |
+
gr.on(
|
| 323 |
+
triggers=[run_button.click, prompt.submit],
|
| 324 |
+
fn=infer,
|
| 325 |
+
inputs=[
|
| 326 |
+
input_images,
|
| 327 |
+
prompt,
|
| 328 |
+
seed,
|
| 329 |
+
randomize_seed,
|
| 330 |
+
true_guidance_scale,
|
| 331 |
+
num_inference_steps,
|
| 332 |
+
height,
|
| 333 |
+
width,
|
| 334 |
+
rewrite_prompt,
|
| 335 |
+
num_images_per_prompt,
|
| 336 |
+
],
|
| 337 |
+
outputs=[result, seed],
|
| 338 |
+
)
|
| 339 |
+
|
| 340 |
+
if __name__ == "__main__":
|
| 341 |
+
demo.launch()
|
requirements.txt
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
git+https://github.com/huggingface/diffusers.git
|
| 2 |
+
transformers
|
| 3 |
+
accelerate
|
| 4 |
+
safetensors
|
| 5 |
+
sentencepiece
|
| 6 |
+
dashscope
|
| 7 |
+
kernels
|